Method to predict session duration on mobile devices using native machine learning

ABSTRACT

Methods and apparatus for predicting time spans for mobile platform activation are presented. A machine-learning service executing on a mobile platform receives feature-related data. The feature-related data includes usage-related data about time spans that the mobile platform is activated and platform-related data received from the mobile platform. The usage-related data and the platform-related data can differ. The machine-learning service determines whether the machine-learning service is trained to perform machine-learning operations related to predicting a time span that the mobile platform will be activated. In response to determining that the machine-learning service is trained, the machine-learning service: receives a request for a predicted time span that the mobile platform will be activated, determines the predicted time span by the machine-learning service performing a machine-learning operation on the feature-related data, and sends the predicted time span.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Patent App. No. 61/663,421,entitled “Method to Predict Session Learning on Mobile Devices UsingNative Machine Learning”, filed Jun. 22, 2012, the contents of which arefully incorporated by reference herein for all purposes.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

Mobile devices are ubiquitous in modern communication networks. Many ofthese mobile devices are “smart phones”, capable of running one or moreapplications while acting as a communication device. The applicationsand/or the smart phone itself can have a number of settings subject touser control, such as volume settings, network addresses/names, contactdata, and calendar information.

A smart phone user can change some or all of these settings based ontheir context, such as location and activity. For example, the user canturn down a ringing volume and/or mute a ringer, prior to watching amovie at a movie theater. After the movie theater completes, the usercan the turn up the ringing volume and/or un-mute the ringer.

SUMMARY

In one aspect, a method is provided. A machine-learning serviceexecuting on a mobile platform receives data related to a plurality offeatures. The machine-learning service determines at least one featurein the plurality of features based on the received data. Themachine-learning service generates an output by performing amachine-learning operation on the at least one feature of the pluralityof features. The machine-learning operation is selected from among: anoperation of ranking the at least one feature, an operation ofclassifying the at least one feature, an operation of predicting the atleast one feature, and an operation of clustering the at least onefeature. The machine-learning service sends the output.

In another aspect, a method is provided. A machine-learning serviceexecuting on a mobile platform receives feature-related data related toa plurality of features. The feature-related data includes data relatedto a first plurality of features received from an application executingon the mobile platform and data related to a second plurality offeatures received from the mobile platform. The first plurality offeatures and the second plurality of features differ. Themachine-learning service determines at least one feature among the firstplurality of features and the second plurality of feature based on thefeature-related data. The machine-learning service generates an outputby performing a machine-learning operation on the at least one feature.The machine-learning service sends the output to the application.

In even another aspect, an article of manufacture is provided. Thearticle of manufacture includes a non-transitory computer-readablestorage medium having instructions stored thereon that, when executed onby a processor, cause the processor to perform functions. The functionsinclude: (a) receiving data related to a plurality of features, (b)determining at least one feature in the plurality of features based onthe received data, (c) generating an output by performing amachine-learning operation on the at least one feature of the pluralityof features, where the machine-learning operation is selected fromamong: an operation of ranking the at least one feature, an operation ofclassifying the at least one feature, an operation of predicting the atleast one feature, and an operation of clustering the at least onefeature, and (d) sending the output.

In yet another aspect, a mobile platform is provided. The mobileplatform includes a processor and a non-transitory computer-readablestorage medium, configured to store instructions that, when executed bythe processor, cause the mobile platform to perform functions. Thefunctions include: (a) receiving data related to a plurality offeatures, (b) determining at least one feature in the plurality offeatures based on the received data, (c) generating an output byperforming a machine-learning operation on the at least one feature ofthe plurality of features, where the machine-learning operation isselected from among: an operation of ranking the at least one feature,an operation of classifying the at least one feature, an operation ofpredicting the at least one feature, and an operation of clustering theat least one feature, and (d) sending the output.

In a second aspect, a method is provided. A context-identificationsystem executing on a mobile platform receives data comprising:context-related data associated with the mobile platform andapplication-related data received from the mobile platform. Thecontext-identification system identifies at least one context using thecontext-related data associated with the mobile platform and/or theapplication-related data received from the mobile platform. Based on atleast one context identified, the context-identification system predictsa communicative action associated with the mobile platform by performinga machine-learning operation on the received data. An instruction isreceived to execute the communicative action associated with the mobileplatform.

In another aspect, an article of manufacture is provided. The article ofmanufacture includes a non-transitory computer-readable storage mediumhaving instructions stored thereon that, when executed on by aprocessor, cause the processor to perform functions. The functionsinclude: (a) receiving data at a context-identification system executingon a mobile platform, where the received data includes context-relateddata associated with the mobile platform and application-related datareceived from the mobile platform, (b) identifying a context using thecontext-related data associated with the mobile platform and/or theapplication-related data received from the mobile platform, (c) based onat least one context identified, predicting a communicative actionassociated with the mobile platform by performing a machine-learningoperation on the data received; and (d) receiving an instruction toexecute the communicative action associated with the mobile platform.

In yet another aspect, a mobile platform is provided. The mobileplatform includes a processor and a non-transitory computer-readablestorage medium, configured to store instructions that, when executed bythe processor, cause the mobile platform to perform functions. (a)receiving data at a context-identification system executing on a mobileplatform, where the received data includes context-related dataassociated with the mobile platform and application-related datareceived from the mobile platform, (b) identifying a context using thecontext-related data associated with the mobile platform and/or theapplication-related data received from the mobile platform, (c) based onat least one context identified, predicting a communicative actionassociated with the mobile platform by performing a machine-learningoperation on the data received; and (d) receiving an instruction toexecute the communicative action associated with the mobile platform.

In a third aspect, a method is provided. A machine-learning serviceexecuting on a mobile platform receives feature-related data. Thefeature-related data includes image-related data related to one or moreimages received from an application executing on the mobile platform andplatform-related data received from the mobile platform. Theimage-related data and the platform-related data differ. Themachine-learning service generates a title related to the one or moreimages by performing a machine-learning operation on the feature-relateddata. The machine-learning service sends the title related to the one ormore images to the application.

In another aspect, an article of manufacture is provided. The article ofmanufacture includes a non-transitory computer-readable storage mediumhaving instructions stored thereon that, when executed on by aprocessor, cause the processor to perform functions. The functionsinclude: (i) receiving feature-related data that includes image-relateddata related to one or more images and platform-related data, where theimage-related data and the platform-related data differ, (ii) generatinga title related to the one or more images by performing amachine-learning operation on the feature-related data, and (iii)sending the title related to the one or more images.

In yet another aspect, a mobile platform is provided. The mobileplatform includes a processor and a non-transitory computer-readablestorage medium, configured to store instructions that, when executed bythe processor, cause the mobile platform to perform functions. Thefunctions include: (i) receiving feature-related data that includesimage-related data related to one or more images and platform-relateddata, where the image-related data and the platform-related data differ,(ii) generating a title related to the one or more images by performinga machine-learning operation on the feature-related data, and (iii)sending the title related to the one or more images.

In a fourth aspect, a method is provided. A machine-learning serviceexecuting on a mobile platform receives feature-related data. Thefeature-related data includes communications-related data related to oneor more searches for establishing electronic communications receivedfrom an application executing on the mobile platform andplatform-related data received from the mobile platform. Thecommunications-related data and the platform-related data differ. Themachine-learning service determines whether the machine-learning serviceis trained to perform machine-learning operations related to predictingoutcomes of searches for establishing electronic communications. Inresponse to determining that the machine-learning service is trained:(a) the machine-learning service receives a request for a predictedoutcome of a search for establishing an electronic communication, (b)the machine-learning service generates the predicted outcome byperforming a machine-learning operation on the feature-related data, and(c) the predicted outcome is sent to the application.

In another aspect, an article of manufacture is provided. The article ofmanufacture includes a non-transitory computer-readable storage mediumhaving instructions stored thereon that, when executed on by aprocessor, cause the processor to perform functions. The functionsinclude: (a) receiving feature-related data at a machine-learningservice, where the feature-related data includes communications-relateddata related to one or more searches for establishing electroniccommunications and platform-related data received from the mobileplatform, and where the communications-related data and theplatform-related data differ, (b) determining whether themachine-learning service is trained to perform machine-learningoperations related to predicting outcomes of searches for establishingelectronic communications, (c) in response to determining that themachine-learning service is trained: (i) receiving, at themachine-learning service, a request for a predicted outcome of a searchfor establishing an electronic communication, (ii) generating thepredicted outcome by the machine-learning service performing amachine-learning operation on the feature-related data, and (iii)sending the predicted outcome.

In yet another aspect, a mobile platform is provided. The mobileplatform includes a processor and a non-transitory computer-readablestorage medium, configured to store instructions that, when executed bythe processor, cause the mobile platform to perform functions. Thefunctions include: (a) receiving feature-related data at amachine-learning service, where the feature-related data includescommunications-related data related to one or more searches forestablishing electronic communications and platform-related data, andwhere the communications-related data and the platform-related datadiffer, (b) determining whether or not outcomes of searches forestablishing electronic communications can be predicted, (c) in responseto determining that the outcomes of searches for establishing electroniccommunications can be predicted: (i) receiving a request for a predictedoutcome of a search for establishing an electronic communication, (ii)generating the predicted outcome by performing a machine-learningoperation on the feature-related data, and (iii) sending the predictedoutcome.

In a fifth aspect, a method is provided. A machine-learning serviceexecuting on a mobile platform receives feature-related data. Thefeature-related data includes usage-related data about one or more timespans that the mobile platform is activated and platform-related datareceived from the mobile platform. The usage-related data and theplatform-related data differ. The machine-learning service determineswhether the machine-learning service is trained to performmachine-learning operations related to predicting a time span that themobile platform will be activated. In response to determining that themachine-learning service is trained: (a) the machine-learning servicereceives a request for a predicted time span that the mobile platformwill be activated, (b) the machine-learning service determines thepredicted time span by performing a machine-learning operation on thefeature-related data, and (c) the predicted time span is sent to theapplication.

In another aspect, an article of manufacture is provided. The article ofmanufacture includes a non-transitory computer-readable storage mediumhaving instructions stored thereon that, when executed on by aprocessor, cause the processor to perform functions. The functionsinclude: (a) receiving feature-related data at a machine-learningservice, where the feature-related data includes usage-related dataabout one or more time spans and platform-related data received from themobile platform, and where the usage-related data and theplatform-related data differ, (b) determining whether themachine-learning service is trained to perform machine-learningoperations related to predicting a time span, (c) in response todetermining that the machine-learning service is trained: (i) receiving,at the machine-learning service, a request for a predicted time span,(ii) determining the predicted time span by the machine-learning serviceperforming a machine-learning operation on the feature-related data, and(iii) sending the predicted time span.

In quite another aspect, a mobile platform is provided. The mobileplatform includes a processor and a non-transitory computer-readablestorage medium, configured to store instructions that, when executed bythe processor, cause the mobile platform to perform functions. Thefunctions include: (a) receiving feature-related data at amachine-learning service, where the feature-related data includesusage-related data about one or more time spans that the mobile platformis activated and platform-related data, and where the usage-related dataand the platform-related data differ, (b) determining whether themachine-learning service is trained to perform machine-learningoperations related to predicting a time span that the mobile platformwill be activated, (c) in response to determining that themachine-learning service is trained: (i) receiving a request for apredicted time span that the mobile platform will be activated at themachine-learning service, (ii) determining the predicted time span bythe machine-learning service performing a machine-learning operation onthe feature-related data, and (iii) sending the predicted time span.

In a sixth aspect, a method is provided. A machine-learning serviceexecuting on a mobile platform receives feature-related data. Thefeature-related data includes volume-related data about one or morevolume-related settings for the mobile platform and platform-relateddata received from the mobile platform. The volume-related data and theplatform-related data differ. The machine-learning service determineswhether the machine-learning service is trained to performmachine-learning operations related to predicting a change in the one ormore volume-related settings for the mobile platform. In response todetermining that the machine-learning service is trained: (a) themachine-learning service receives a request for predicting the change inthe one or more volume-related settings, (b) the machine-learningservice determining the predicted change in the one or morevolume-related settings by performing a machine-learning operation onthe feature-related data, and (c) sending the predicted change in theone or more volume-related settings.

In another aspect, an article of manufacture is provided. The article ofmanufacture includes a non-transitory computer-readable storage mediumhaving instructions stored thereon that, when executed on by aprocessor, cause the processor to perform functions. The functionsinclude: (a) receiving feature-related data at a machine-learningservice executing on the processor, where the feature-related dataincludes volume-related data about one or more volume-related settingsand platform-related data, and where the usage-related data and theplatform-related data differ, (b) determining whether themachine-learning service is trained to perform machine-learningoperations related to predicting a change in the one or morevolume-related settings, (c) in response to determining that themachine-learning service is trained: (i) receiving, at themachine-learning service, a request for predicting the change in the oneor more volume-related settings, (ii) determining the predicted changein the one or more volume-related settings by the machine-learningservice performing a machine-learning operation on the feature-relateddata, and (iii) sending the predicted change in the one or morevolume-related settings.

In yet another aspect, a mobile platform is provided. The mobileplatform includes a processor and a non-transitory computer-readablestorage medium, configured to store instructions that, when executed bythe processor, cause the mobile platform to perform functions. Thefunctions include: (a) receiving feature-related data at amachine-learning service executing on the processor, where thefeature-related data includes volume-related data about one or morevolume-related settings and platform-related data received from themobile platform, and where the usage-related data and theplatform-related data differ, (b) determining whether themachine-learning service is trained to perform machine-learningoperations related to predicting a change in the one or morevolume-related settings for the mobile platform, (c) in response todetermining that the machine-learning service is trained: (i) receiving,at the machine-learning service, a request for predicting the change inthe one or more volume-related settings, (ii) determining the predictedchange in the one or more volume-related settings by themachine-learning service performing a machine-learning operation on thefeature-related data, and (iii) sending the predicted change in the oneor more volume-related settings.

BRIEF DESCRIPTION OF THE FIGURES

In the figures:

FIG. 1A is a flow chart of a method, in accordance with an exampleembodiment.

FIG. 1B is a flow chart of another method, in accordance with an exampleembodiment.

FIG. 2 is a block diagram of a mobile platform, in accordance with anexample embodiment.

FIG. 3 is a block diagram of a machine learning and adaptation service,in accordance with an example embodiment.

FIG. 4 depicts programming interfaces for the machine learning andadaptation service, in accordance with an example embodiment.

FIG. 5A shows an example user interface used in a first scenario, inaccordance with an example embodiment.

FIG. 5B shows example communications used in the first scenario, inaccordance with an example embodiment.

FIG. 6A shows an example user interface used in a second scenario, inaccordance with an example embodiment.

FIG. 6B shows example communications for the second scenario, inaccordance with an example embodiment.

FIG. 7 is a flow chart of a method, in accordance with an exampleembodiment.

FIG. 8A shows an example user interface used in a third scenario, inaccordance with an example embodiment.

FIG. 8B shows an example data record, in accordance with an exampleembodiment.

FIG. 9A shows an example context identification system, in accordancewith an example embodiment.

FIG. 9B shows an example collective context identification system, inaccordance with an example embodiment.

FIG. 9C shows an example context identification system adaptable tofeedback, in accordance with an example embodiment.

FIG. 10 is a flow chart of a method, in accordance with an exampleembodiment.

FIG. 11A shows an example image identification system, in accordancewith an example embodiment.

FIG. 11B shows an example collective image identification system, inaccordance with an example embodiment.

FIG. 11C shows an example image identification system adaptable tofeedback, in accordance with an example embodiment.

FIG. 12A shows an example user interface used in a fourth scenario, inaccordance with an example embodiment.

FIG. 12B shows example communications for the fourth scenario, inaccordance with an example embodiment.

FIG. 13 is a flow chart of a method, in accordance with an exampleembodiment.

FIG. 14 shows a scenario for establishing wireless communications with anumber of access points, in accordance with an example embodiment.

FIG. 15A shows an example user interface used in a fifth scenario, inaccordance with an example embodiment.

FIG. 15B shows example communications for the fifth scenario, inaccordance with an example embodiment.

FIG. 16 is a flow chart of a method, in accordance with an exampleembodiment.

FIG. 17A shows a usage log of activations of a mobile platform, inaccordance with an example embodiment.

FIG. 17B shows a usage log of activations of a mobile platform at aparticular location, in accordance with an example embodiment.

FIG. 17C is a graph of a linear regression model based on the data inthe usage log shown in FIG. 17B, in accordance with an exampleembodiment.

FIG. 18A is a classification diagram based on the data in the log shownin FIG. 17A, in accordance with an example embodiment.

FIG. 18B is an example user interface for a jukebox application used ina sixth scenario, in accordance with an example embodiment.

FIG. 19 is a flow chart of a method, in accordance with an exampleembodiment.

FIG. 20A shows an example user interface used in a seventh scenario, inaccordance with an example embodiment.

FIG. 20B shows example communications for the seventh scenario, inaccordance with an example embodiment.

FIG. 21 depicts a distributed computing architecture, in accordance withan example embodiment.

FIG. 22A is a block diagram of a computing device, in accordance with anexample embodiment.

FIG. 22B depicts a cloud-based server system, in accordance with anexample embodiment.

DETAILED DESCRIPTION Overview

Most mobile platforms, such as mobile phones, do not adapt to theperson(s) using the platform; rather, the person(s) adapt to use themobile platform. Typically, customization of the mobile platform and theapplications utilizing the mobile platform is often limited based onimplementation decisions.

A “machine-learning service” or machine-learning and adaptation servicecan support automatic adaptation of preferences of person(s) using themobile platform. The machine-learning service is software running on themobile platform that provides the necessary functionality for softwareapplications to learn from interactions of person(s) using the mobileplatform.

The machine-learning service can communicate with software applicationsvia an Application Program Interface (API). The API provides access toseveral commonly-used machine adaptation techniques. For example, theAPI can provide access to interfaces for ranking, clustering,classifying, and prediction techniques. Also, a software application canprovide one or more inputs to the machine-learning service. For example,a software application controlling a volume setting of a speaker canprovide volume setting values as an input to the machine-learningservice.

The API can also utilize data that the software application does not andperhaps cannot access. For example, a volume-setting application may nothave access to location data for the mobile platform. However, thevolume-setting application could request the machine-learning service topredict the volume setting of the speaker based on location. Themachine-learning service need only provide the predictions, not thelocations, to the volume-setting application. As such, themachine-learning service can encapsulate the use of sensitive data.

The machine-learning service can include a data aggregation andrepresentation engine (DARE) that constantly receives and stores inputdata, perhaps from multiple sources. The stored input data can beaggregated to discover features within the data; such as location labelsbased on actions and times; e.g. Home, Work, School, etc.

The machine adaptation techniques used by the machine-learning servicecan be implemented to work best within the processing, memory, and otherresource constraints of a mobile platform. For example, the machineadaptation techniques can use incremental learning algorithms thatrequire limited or no historical information for training, and thus mayreduce the total amount of memory needed by themachine-learning-service.

In some embodiments, the machine-learning service can utilize networksupport functionality to access non-private and/or anonymized dataaggregated across multiple mobile platforms. The aggregated data can bestored in one or more servers, or other devices other than the mobileplatform, and downloaded as needed. For example, aggregated data can beused to train and/or set initial values for the machine adaptationtechniques used by the machine-learning service.

Example uses of the machine-learning service can include:

Predicting a duration of a mobile session before the mobile sessionstarts, based on location, time, calendar entries, prior behavior, etc.

Predicting a phone number to be dialed at the onset of utilizing a phonedialing application, based on location, time, calendar entries, priorbehavior, etc.

Predicting speaker and/or mute settings for the mobile platform, basedon location, time, calendar entries, prior behavior, etc.

Classifying locations based on their ability to use Wi-Fi and/or othercommunication networks.

Generating example photo names and photo album names for a cameraapplication utilizing the mobile platform.

Many other examples are possible as well.

By providing application access to a number of machine adaptationtechniques designed to operate on and learn about user behavior of amobile platform, the machine-learning service can make mobile platformseasier to use, more efficient from a user's point of view, and saveusers time and effort in utilizing the variety of applications availableon the mobile platform.

Example Operations

Turning to the figures, FIG. 1A is a flow chart of method 100, inaccordance with an example embodiment. In some embodiments, part or allof method 100 can be executed using one or more mobile platforms; e.g.,mobile platform 200, 502, 602, 802, 1202, 1502, 1852, 2002 and/or one ormore computing devices; e.g., computing device 2200.

Method 100 begins at block 110, where a machine-learning serviceexecuting on a mobile platform can receive data related to a pluralityof features. In some embodiments, the received data can include dataaggregated from a plurality of mobile platforms.

At block 120, the machine-learning service can determine at least onefeature in the plurality of features based on the received data.

At block 130, the machine-learning service can generate an output by theperforming a machine-learning operation on the at least one feature ofthe plurality of features. The machine-learning operation can beselected from among: an operation of ranking the at least one feature,an operation of classifying the at least one feature, an operation ofpredicting the at least one feature, and an operation of clustering theat least one feature.

In some embodiments, generating the output by the machine-learningservice can include selecting a machine-learning algorithm to performthe machine-learning operation. In particular embodiments, selecting themachine-learning algorithm to perform the machine-learning operation caninclude the machine-learning service selecting the machine-learningalgorithm.

In some of the particular embodiments, selecting the machine-learningalgorithm to perform the machine-learning operation can includereceiving a selection related to the machine-learning algorithm from anapplication executing on the mobile platform, where the applicationdiffers from the machine-learning service. In specific of the particularembodiments, receiving a selection related to the machine-learningalgorithm from the application can include receiving a selection relatedto the machine-learning algorithm from the application via anApplication Programming Interface (API) of the machine-learning service.

At block 140, the machine-learning service can send the output. In someembodiments, the received data can include data related to a pluralityof features related to communication signals and locations, where themobile platform is located at a first location, and where the outputincludes an indication of whether to search for the communicationssignal at the first location.

In other embodiments, the received data can include data related to aplurality of features related to durations of communication sessions andthe output can include a prediction of a duration of a new communicationsession. In still other embodiments, the received data can include datarelated to features of telephone calls originated by the mobile platformand the output can include a called party of a telephone call to beoriginated by the mobile platform. In even other embodiments, thereceived data can include data related to volume and mute settings ofthe mobile platform and the output can include a prediction of a volumesetting and/or a mute setting of the mobile platform. In yet otherembodiments, the received data can include data related to one or moreimages, and the output can include a name related to at least one imageof the one or more images.

In some embodiments, method 100 can further include: (i) storing the atleast one feature and the output for use by the machine-learningservice, (ii) receiving second data related to a second plurality offeatures at the machine-learning service, (iii) determining at least asecond feature in the second plurality of features based on the seconddata, (iv) generating a second output by the machine-learning serviceperforming the machine-learning operation on the at least the secondfeature and the stored at least one feature and output, and (v) sendingthe second output from the machine-learning service.

FIG. 1B is a flow chart of method 150, in accordance with an exampleembodiment. In some embodiments, part or all of method 150 can beexecuted using one or more mobile platforms; e.g., mobile platform 200,502, 602, 802, 1202, 1502, 1852, 2002 and/or one or more computingdevices; e.g., computing device 2200.

Method 150 begins at block 160, where a machine-learning serviceexecuting on a mobile platform can receive feature-related data. Thefeature-related data can include data related to a first plurality offeatures received from an application executing on the mobile platformand data related to a second plurality of features received from themobile platform. The first plurality of features and the secondplurality of features can differ.

In some embodiments, the first plurality of features can include afeature selected from among a time, a location, a duration, a signalstrength, a power level, a party of a telephone call, an e-mail address,a contact, and a calendar entry. In other embodiments, the secondplurality of features can include a feature selected from among animage-related feature and a speech-related feature.

At block 170, the machine-learning service can determine at least onefeature from among the first plurality of features and the secondplurality of features based on the feature-related data.

At block 180, the machine-learning service can generate an output byperforming a machine-learning operation on the at least one feature ofthe plurality of features.

At block 190, the machine-learning service can send the output to theapplication.

Example Machine Learning and Adaptation Service

FIG. 2 is a block diagram of mobile platform 200, in accordance with anexample embodiment. Mobile platform 200 includes mobile platform system210, machine learning and adaptation service 220, system application(s)230, and user application(s) 240.

FIG. 2 shows that mobile platform system 210 includes mobile platformhardware 212 and operating system software 214. Mobile platform hardware212 can include hardware of a computing device, such as computing device2200 described below. Operating system software 214 can include, but isnot limited to software configured to utilize mobile platform hardware212, provide user services such as user interfaces for the mobileplatform, allocate resources, and schedule processes and/or threads. Anexample of operating system software 214 is the Android operating systemdeveloped by Google Inc. of Mountain View, Calif. Additional hardwareand/or software can be used by mobile platform system 210 as well.

Application processes, such as system application 230 and userapplication 240, can execute using mobile platform system 210. Systemapplication 230 can be an application provided by a provider of part orall of mobile platform system 210. User application 240 can be anapplication provided by an entity other than a provider of part or allof mobile platform system 210

Machine learning and adaptation service 220 can be configured to providethe functionality of a machine-learning service. FIG. 2 shows thatmachine learning and adaptation service 220 can communicate with bothmobile platform system 210 and applications 230, 240 via feature-relateddata (FRD) and machine-learning operation output (MLOO).

Feature-related data can be “built-in” or provided by mobile platformsystem 210; e.g., as feature-related data 222. Built-in feature relateddata can include, but are not limited to, times, dates, locations,settings for mobile platform hardware 212 (ringing volume, mutesettings, ring tones, brightness levels, power/battery data, etc.),calling data (calling party, called party, dialed digits, calling state,etc.), power/battery data, communication network data (addresses,networks, login/password information, signal strengths, etc.), currentapplication(s) being executed, and user preference information.

Machine learning and adaptation service 220 can determine one or morefeatures from feature-related data 222. For example, suppose thatfeature-related data 222 includes the text of a Short Message Service(SMS) message stating “I′m outta here.” Machine learning and adaptationservice 220 can generate features represented as feature vectors. Anexample feature vector for SMS messages can be a <word, frequency> pair.Using the example SMS message text, a set of corresponding examplefeature vectors can be {<I'm, 1>, <outta, 1>, <here, 1>}. As anotherexample, suppose text of an e-mail reads “Scott—Either we go now, or wego after John's talk”, then a set of corresponding example featurevectors can be {<Scott-, 1>,<Either, 1>, <we, 2>, <go, 2>, <now, 1>,<or, 1>, <after, 1>, <John's, 1>, <talk, 1>}.

As another example, suppose a list of suggested labels W1, W2, W3 . . .Wn were provided to a user with a request to “click on” the one bestrepresenting the user's current location. The user clicked on label W4to select W4 as the closest representation. Then, perhaps as part of atraining exercise, the list of suggested labels can be provided asfeatures to machine learning and adaptation service 220. An examplefeature can be a <higher ranked feature, lower ranked feature> featurevector, and as applied to the list of suggested labels, the followingfeature vectors can be determined <W4, W1>, <W4, W2>, <W4, W3>, <W4, W5>. . . <W4, Wn>. Many other examples of features, feature vectors, andfeature-related data are possible as well.

In some embodiments, feature-related data can include commands tomachine learning and adaptation service 220, such as a command to“train” or learn about input data and perform one or more machinelearning operations on the learned input data. For example, machinelearning and adaptation service 220 can receive a command to cluster aseries of locations, which can be provided as latitude/longitude pairs,perhaps using cluster labels such as “Work”, “Home”, “School”, and“Gym”. Then, once trained, machine learning and adaptation service 220can output one or more machine-learning operation outputs 224.Continuing the location example mentioned above, given an inputlatitude/longitude pair as part of feature-related data 222, thecorresponding machine-learning operation output 224 can be a clusterlabel.

Machine learning and adaptation service 220 can also train with andutilize feature-related data provided by system application 230 asfeature-related data 232 and/or feature-related data provided by userapplication 240 as feature-related data 242. Then, upon receiving inputdata, machine learning and adaptation service 220 can providemachine-learning operation output(s) to system application 230 asmachine-learning operation output 234 and/or provide machine-learningoperation output(s) to user application 240 as machine-learningoperation output 244. That is, machine learning and adaptation service220 need not have detailed information about feature-related data 222,232, 242 a priori; rather machine learning and adaptation service 220can utilize incremental learning techniques to generate a model offeature-related data 222, 232, 242, and update the model as needed basedon feature-related data 222, 232, 242.

Additionally, machine learning and adaptation service 220 need not havedetailed information about applications 230, 240 a priori. Rather,applications 230, 240 can use interface(s) to machine learning andadaptation service 220 to permit use of machine learning and adaptationservice 220 as a toolkit of machine-learning techniques.

FIG. 3 is a block diagram of machine learning and adaptation service220, in accordance with an example embodiment. Machine learning andadaptation service 220 includes machine learning and adaptation serviceAPI 310, machine learning and adaptation engine (MLAE) 312, dataaggregation and representation engine (DARE) 314, service manager 316,and machine learning and adaptation service (MLAS) network support 320.

Machine learning and adaptation service API 310 provides interfaces toaccess a number of machine learning and adaptation techniques and toexchange data with machine learning and adaptation service 220. In someembodiments, models for machine learning and adaptation can be saved andloaded via machine learning and adaptation service API 310.

Machine learning and adaptation engine 312 performs the machine learningand adaptation techniques of machine learning and adaptation service220. In some embodiments, machine learning and adaptation engine 312 canlearn data from one or more sources, and then classify, cluster, rank,and/or predict data from given input data.

Classifying data involves putting data with a number N of possiblevalues into one of C1 pre-determined categories, where C1 is finite. Foran example with C1=2, a mobile platform can be classified, for eachvalue T of a set of times, into one of two categories: either “poweredup” or “not powered up”. As another example with C1=3, a locationspecified as a latitude/longitude pair (or via another technique) can beclassified into one of three categories: “having a strong accessibleWi-Fi signal”, “having an adequate accessible Wi-Fi signal”, or “havinglittle or no accessible Wi-Fi signal.” Many other examples are possibleas well.

Classification can be performed using one or more statisticalclassification techniques, such as, but not limited to, linearclassifiers, support vector machines, quadratic classifiers, kernelestimation, decision trees, neural networks, Bayesian techniques and/ornetworks, hidden Markov models, binary classifiers, and/or multi-classclassifiers.

Clustering data involves putting data with a number N of possible valuesinto one of C2 clusters, where C2 is finite, and where the clusters arenot necessarily pre-determined. Generally, each data item in a givencluster is more similar to each other than to data item(s) in othercluster(s). For example, a mobile platform can track its locationthroughout the day to find clusters of locations where the mobileplatform can be commonly found, such as work location(s), homelocation(s), shopping location(s), entertainment location(s), and otherlocation(s). Location clusters can vary from person to person; anattorney who works at a law office and then frequents a restaurant mayconsider the law office as a “work location” and the restaurant as an“entertainment location”, but chef may consider the law office as an“other location” and the restaurant as a “work location.”

Clustering can be performed using one or more clustering algorithms,such as, but not limited to, connectivity-based clustering, hierarchicalclustering, centroid-based clustering, distribution-based clustering,density-based clustering, and partitioning algorithms.

Ranking a set of data items of size S involves applying a rankingfunction RF to the S data items and returning the highest (or lowest) Nranked data items, where 0<N≦S. For example, suppose a co-ed volleyballteam had the following statistics for points scored:

TABLE 1 Name Points Scored Pat 10 Chris A. 9 Kelly 8 Taylor 7 Alex 6Drew 5 M.C. 4 Jaden 3 Chris B. 2 Lucky 1

Let the ranking function RF rank the N=10 team players listed in Table 1from 1 to N based on points scored. For this example, RF(“Pat”)=1,RF(“Chris A”)=2, and RF(“Lucky”)=10.

Another ranking example is ranking documents based on a query of one ormore keywords. The ranking of a document D out of a total of N documentscan express the relative relevance of document D to the query. In someembodiments, rankings can have two or more partitions or levels. Forexample, given a query with keywords K1, K2, . . . Kn, documents canfirst be ranked as relevant or irrelevant based on keywords K1-Kn. Then,a second ranking function can rank the subset of relevant documents,perhaps in more detail.

Ranking can be performed using one or more ranking algorithms, such as,but not limited to, instance ranking algorithms, label rankingalgorithms, subset ranking algorithms, rank aggregation algorithms,bipartite/k-partite ranking algorithms, and learning-to-rank algorithms.

Predicting data can involve determining a predicted value given aprevious pattern of values. For example, given that the previous N=20values were: 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,and 2, the predicted value may be 1. Prediction can be performed usingone or more prediction algorithms, such as, but not limited to, minimax,decision tree algorithms, classification tree algorithms, linearregression, polynomial regression, statistical regression, curvefitting, interpolation, and maximum likelihood estimation algorithms.

Data aggregation and representation engine 314 aggregates, or combines,data from applications and the mobile platform system and stores theaggregated data in a model accessible to machine learning and adaptationengine 312. In some scenarios, aggregation of data enables discovery ofnew inferences based on the combined data that were difficult to observein the raw data. For example, suppose a mobile platform is located inthe labeled locations listed in Table 2 below:

TABLE 2 Location % of Time at Location Work 42 Home 38 Other 12 Store 13 Store 2 1 Bart 1 Jill 1 Mom/Dad 1 Maureen 1

Assuming a large amount of the “Other” location time is time spentdriving, these data indicate that the mobile platform is at work, home,or traveling. That is, the mobile platform spends 80% of the time atwork and home and more than 90% of the time at work, home, or (likely)in a vehicle. Based on this information, the mobile platform likely haseasy access to commercial electrical power at least 80% of the time, andso battery life may not be as important to this mobile platform, ascompared to, say, being able to recharge a battery of the mobile phonefrom commercial electrical power and while driving.

Service manager 316 can manage learning session(s), or instances of oneor more machine learning and adaptation models, for the application(s)utilizing machine learning and adaptation service 220. Service manager316 can also coordinate permissions for access and/or display of data.In some embodiments, service manager 316 can prioritize learningsessions and/or resources used by machine learning and adaptationservice 220

Machine learning and adaptation service network support 320 can provide“back end” support to access non-private and/or anonymized data that hasbeen authorized to be gathered from a plurality of mobile platforms. Insome scenarios, the non-private and/or anonymized data can be residenton mobile platform 200, while in other scenarios, part or all of thenon-private and/or anonymized data can reside on other devices, such asservers and/or other mobile platforms. In the other scenarios, mobileplatform 200 may temporarily or permanently store the non-private and/oranonymized data, perhaps depending on the access to the non-privateand/or anonymized data provided to mobile platform 200 and/or machinelearning and adaptation service 220.

As the non-private and/or anonymized data is administered by machinelearning and adaptation service network support 320, the non-privateand/or anonymized data can be used by machine learning and adaptationservice 220, but not shared or “hidden” from applications. In someembodiments not shown in FIG. 3, a built-in manager can enable access tobuilt-in features for machine learning and adaptation service 220 whilehiding some or all built-in features from applications.

FIG. 4 depicts programming interfaces 400 related to machine learningand adaptation service 200, in accordance with an example embodiment.Applications 402 a, 402 b, 402 c communicate with service manager 316 tomanage learning sessions. Each learning session is shown in FIG. 4 as alearning session thread. For example, learning session threads 432 a,432 b, 432 c respectively communicate with applications 402 a, 402 b,and 402 c to conduct a learning session. In some embodiments not shownin FIG. 4, an application can conduct zero learning sessions or conductmultiple learning sessions simultaneously.

FIG. 4 shows that service manager 316 includes built-in features 410,learning application programming interface (API) 412, and sessioninformation 420. In embodiments not shown in FIG. 4, built-in features410, learning API 412, and/or session information 420 can be componentsof other portions of machine learning and adaptation service 220. Instill other embodiments not shown in FIG. 4, built-in features 410,learning API 412, and/or session information 420 can be separatecomponents of machine learning and adaptation service 220.

As FIG. 4 shows just below learning sessions 432 a-43 c, built-infeatures 410 can provide various types of data to service manager 316and, more generally, to machine learning and adaptation service 220.FIG. 4 shows examples of data provided by built-in features 410, such aslocation, location labels, application statistics, people-relatedfeatures, calling information, battery/power data, network information,and historical/usage information. Locations can be specified in terms oflatitude/longitude pairs, latitude/longitude/altitude triples, streetaddresses, intersections, and/or using other location-specifying data.Location labels can include names such as the “Locations” shown in Table2 above; e.g., work, home, stores, etc.

Application statistics can include a time T an application App was lastexecuted, a location loc an application was last executed, a probabilityP(App|T) that the application App is executing at a given time, aprobability P(App|loc) that App is executing at a given location, and aprobability P(App|T & loc) that App is executing at a given location ata given time. The above-mentioned application statistics can begenerated by statistical software operating on data regardingapplication execution times and locations. Other statistics can bedetermined as application statistics as well or instead.

Information about people can be determined from contact information,calendar information, social networking accounts and pages, and fromother sources. As part of built-in features 410, information such as,but not limited to a person's name, address, phone number(s), e-mailaddress(es), social networking account information can be provided tomachine learning and adaptation service 220.

Calling information can include per-call information such as callingparty, dialed digits/called party information, call duration, call starttime, and call end time. In some cases, calling information can also orinstead include contact information, such as a name and/or “Caller-ID”data associated with a called party, and/or historical callinginformation for a number of previous calls NPC, where NPC>0.

In embodiments not shown in FIG. 4, other types of communicationinformation can be provided as part of built-in features 410. Examplesof other types of communication information include, but are not limitedto, e-mail information, SMS information, blog information, and socialnetworking messaging information.

Battery/power information can include, but is not limited to,information about batteries and/or power for the mobile platform, suchas battery levels, battery status (e.g., operating, disconnected,charging), commercial power status (e.g., connected, charging,disconnected), time(s) of connection(s) to and/or disconnection(s) fromcommercial power. Network information can include, but is not limitedto, information about telecommunications networks, local area networks,other networks, network addresses, connection time(s), disconnectiontime(s), and network status (e.g., connecting, connected, acquiringaddress(es), communicating data, idle, disconnecting, disconnected).

Historical/usage information can include, but is not limited to,information about previous usage of machine learning and adaptationservice 220. Example information includes a number of features “pushed”or provided to machine learning and adaptation service 220, a number ofitems “pulled” or requested from machine learning and adaptation service220, a number of applications currently using machine learning andadaptation service 220, a number of applications that have used machinelearning and adaptation service 220 in a unit period of time (e.g., anumber of application using machine learning and adaptation service 220per hour or per day), and maximum and/or minimum numbers of applicationscurrently using machine learning and adaptation service 220.

Learning API 412 can include at least the functions shown in FIG. 4:getService( ), RankerInterface( ), PredictionInterface( ),ClassificationInterface( ), and ClusteringInterface( ). In someembodiments, more or fewer functions can be part of learning API 412. Anapplication can call the getService( ) function to initialize and starta learning session; e.g., initialize and create a learning sessionthread. The getService( ) function can return a learning session keythat can specify a specific learning session.

FIG. 4 shows learning API 412 with four example learning interfaces:RankerInterface( ), PredictionInterface( ), ClassificationInterface( ),and ClusteringInterface( ). In some embodiments, more or fewer learninginterfaces can be part of learning API 412.

An application can call the RankerInterface( ) function to initializeand generate a ranking model of machine learning and adaptation service220. The ranking model can be used to order or rank a set of data itemsof size S by applying a ranking function RF to the S data items andreturning the highest (or lowest) N ranked data items, such as discussedabove in the context of FIG. 3. An application can call thePredictionInterface( ) function to initialize and generate a predictionmodel of machine learning and adaptation service 220. The predictionmodel can be used to determine a predicted value given a previouspattern of values, such as discussed above in the context of FIG. 3.

An application can call the Classificationlnterface( ) function toinitialize and generate a classification model of machine learning andadaptation service 220. The classification model can be used to put datawith a number N of possible values into one of C1 pre-determinedcategories, where C1 is finite, such as discussed above in the contextof FIG. 3. An application can call the ClusteringInterface( ) functionto initialize and generate a clustering model of machine learning andadaptation service 220. The clustering model can be used to put datawith a number N of possible values into one of C2 clusters, where C2 isfinite, and where the clusters are not necessarily pre-determined, suchas discussed above in the context of FIG. 3.

In some embodiments, an application can use only one learning interfaceper learning session, while in other embodiments, the application canuse multiple learning interfaces per learning session.

Each of the learning interfaces shown in learning API 412 can include atleast the functions of example learning interface 440. FIG. 4 showslearning interface 440 with five functions: Push( ) Pull( ) Notify( ),Save( ) and Load( ) In some embodiments, more or fewer functions can bepart of learning interface 440.

The Push( ) function can enable an application to provide data to alearning session. The Pull( ) function can enable an application torequest and receive information, such as inference(s) and/orprediction(s), from a learning session.

The Notify( ) function can enable an application to receive information,such as inference(s) and/or prediction(s), from a learning sessionwithout requesting the information. For example, suppose a learningsession is informed about an event of interest to the application andconsequently uses its learning model to generate a prediction for use bythe application. In this example, the application has not requested theprediction, i.e., the application did not call the Pull( ) function.Therefore, the learning session can provide the prediction to theapplication using the Notify( ) function.

The Save( ) function can save learning-model data needed tostart/restart a learning model, and provide a copy of the learning-modeldata to the application. The Load( ) function can take a copy oflearning-model data from the application and to start/restart a learningmodel with the learning-model data.

Session information 420 can include information about each learningsession conducted by machine learning and adaptation service 220.Example session information 420 can include, but is not limited to, alearning session key for the learning session, information about theapplication such as an application name, application addressinginformation, type(s) of learning interface(s) used in the learningsession (e.g., RankerInterface( ) PredictionInterface( ), usageinformation for the learning interface(s), and perhaps data stored bythe learning model for the learning session, such as a learning modelcache.

As mentioned above, service manager 316 can manage learning session(s)for the application(s) utilizing machine learning and adaptation service220. Specifically, as shown in FIG. 4, service manager can enablecommunications between applications 402 a-402 c, data aggregation andrepresentation engines 314 a, 314 b and learning session threads 432a-432 c. As each application 402 a-402 c is associated with onerespective learning session thread 432 a-432 c, service manager 316 canensure that information from a given application is directed to thecorresponding learning session thread, and not routed to anotherlearning session thread, and vice versa. One technique to ensure thatinformation from a given application is directed to the correspondinglearning session thread is to verify that a learning session key iscommon to both the given application and the corresponding learningsession thread. In some embodiments, session information 420 can includea mapping between session keys, applications, and learning sessionthreads.

Each of data aggregation and representation engines 314 a, 314 baggregates, or combines, data from applications 402 a-402 c and themobile platform system and stores the aggregated data in a modelaccessible to appropriate learning session thread(s) 432 a-432 c, suchas discussed above in the context of FIG. 3.

Each of learning session threads 432 a, 432 b, and 432 c implements alearning model. FIG. 4 shows that learning session threads 432 a, 432 b,and 432 c each respectively include: machine learning and adaptationengine (MLAE) 312 a, 312 b, 312 c, feature buffer 434 a. 434 b, 434 c,and context 436 a, 436 b, 436 c. Each of machine learning and adaptationengines 312 a, 312 b, 312 c performs the machine learning and adaptationtechnique discussed above in the context of FIG. 3 and this figure, FIG.4. Each of feature buffers 434 a, 434 b, 434 c stores feature(s) used totrain and/or perform functions for the corresponding machine learningand adaptation engines 312 a, 312 b, 312 c.

Each of contexts 436 a, 436 b, and 436 c stores a learning model thatembodies classifications, predictions, clusterings, or rankings learnedby the corresponding machine learning and adaptation engines 312 a, 312b, 312 c. A respective context can be saved by the application using theSave( ) function of learning interface 440, and the respective contextcan be loaded using the Load( ) function of learning interface 440.

Example Machine Learning and Adaptation Service Usage Scenarios

FIG. 5A shows example user interface 510 used in example scenario 500,in accordance with an example embodiment. Scenario 500 includes userinterface 510 executing on mobile platform 502. As shown in FIG. 5A,user interface 510 includes an interface for dialer application 520 thatincludes microphone dialog 522. Microphone dialog 522 includes manualmicrophone setting 524, smart microphone setting 526, an OK button tosave current settings before exiting dialog 522, and a Cancel button toexit dialog 522 without saving current settings.

Manual microphone setting 524 includes a slider bar that enables a userto set a microphone output setting between a minimum setting of 0, whicheffectively mutes the microphone, and a maximum setting of 100, whichprovides the maximum output volume (a.k.a. maximum gain) for a giveninput. FIG. 5A shows manual microphone setting set to 53, orapproximately halfway between the minimum and maximum settings.

As shown in FIG. 5A, smart microphone setting 526 can learn and setmicrophone volume based on called party and can be set to either enabledor disabled. When smart microphone setting 526 is disabled, dialerapplication 520 can instruct a microphone application (not shown in FIG.5A) to use manual microphone setting 524 to determine output volume fora microphone of mobile platform 502. When smart microphone setting 526is enabled, dialer application 520 can use a learning service of machinelearning and adaptation service 220 to perform a machine-learningoperation to provide setting values for the microphone setting. Then,upon receiving a setting value, dialer application 520 can provide thesetting value to the microphone application, which can then determineoutput volume for the microphone of mobile platform 502 using thesetting value. In scenario 500, and as shown in FIG. 5A, smartmicrophone setting 526 is set to enabled.

FIG. 5B shows example communications for scenario 500, in accordancewith an example embodiment. As part of the initial communications,learning model 530 is partially trained based on data stored in a“DialerModel” variable and then loaded into learning model 530 via aLoad( ) function call. In FIG. 5B, communications between dialerapplication 520 and learning model 530 of machine learning andadaptation service 220 indicate that dialer application 520 had accessto calling party information. As there are no apparent privacy concernswith microphone settings, microphone settings can be requested by dialerapplication 520 and provided by learning model 530. Just beforeconcluding, learning model data is stored back in the DialerModelvariable to preserve information garnered during scenario 500 for lateruse and loading into learning model 530.

In FIG. 5B, scenario 500 continues with dialer application 520 callingthe getService( ) function of learning model 530 via communication 552.In response, learning model 530 provides a session key S1 viacommunication 554 to dialer application 520. Session key S1 is includedfor subsequent communications between dialer application 520 andlearning model 530 to permit addressing the subsequent communications tothe correct learning model, e.g., learning model 530 and correctapplication, e.g., dialer application 520 for a learning session keyedby S1.

Scenario 500 continues with dialer application 520 calling the Load( )function via communication 556 to provide stored learning model data,e.g., a context, stored in a DialerModel variable passed as a parameterof the Load( ) function to learning model 530. Upon receipt ofcommunication 556, learning model 530 can load a context and perhapsother data stored in the DialerModel variable into a learning sessionthread that can be utilized by learning model 530. In some scenarios notshown in FIG. 5B, learning model 530 can provide a response tocommunication 556, such as a return value to the Load( ) function and/ora communication providing a status of the Load( ) function; e.g., LoadOKor LoadFail.

Dialer application 520 can instruct learning model 530, viacommunication 558, to set up a prediction interface. As shown in FIG.5B, communication 558 includes a Predictionlnterface( ) call with threeparameters: session key S1, an input-source reference of CalledParty,and a requested output prediction of microphone volumes, as shown inFIG. 5B, using the predefined MIC_VOL value.

In response, learning model 530 sends communication 560 to built-infeatures 540 to request a built-in feature (ReqBI) of microphone values,as shown in FIG. 5B by passing the predefined MIC_VOL value to built-infeatures 540. Built-in features 540 can include at least thefunctionality of built-in features 410 discussed above. In response,built-in features 540 returns a built-in session key BI1 l viacommunication 562. Built-in session key BI1 is included with subsequentcommunications between learning model 530 and built-in features 540 topermit addressing the subsequent built-in related communications to thecorrect learning model.

FIG. 5B continues with dialer application 520 sending communication 564to learning model 530 with a Push( ) function to inform learning model530 that a call is being made to a called party “Grandma.” Additionally,built-in features 540 sends communication 566, with a Push( ) functionto inform learning model 530 that the microphone volume is being set toa setting of 80.

FIG. 5B shows a number of communications 568-586 for five subsequentcalls made with the calling parties indicated by dialer application 520and microphone volume indicated by built-in features 540 to learningmodel 530 with the values listed in Table 3 below.

TABLE 3 Communications Called Party Microphone Volume 568 and 570 Boss40 572 and 574 Spouse 40 576 and 578 Boss 40 580 and 582 Grandma 82 584and 586 Grandpa 82

Scenario 500 continues with dialer application 520 sendingcommunications 588 and 590 a to learning model 530. Communication 588uses the Push( ) function to inform learning model 530 that a call isbeing made to a called party “Boss.” Communication 590 a requests, viathe Pull( ) function, a predicted value for the MIC_VOL (microphonevolume) setting.

In response to communication 590 a, learning model 530 predicts amicrophone volume setting of 37 based on data (a) stored in theDialerModel variable and loaded as a consequence of communication 556and (b) learned from communications 564-586, particularly communications568, 570, 576, and 578 which relate to calls to a called party of“Boss.” Subsequently, learning model 530 can send a Pull Response(PullResp) as communication 590 b to dialer application 520 providingthe predicted microphone volume setting of 37. In some embodiments, thePull Response can be a return value of the Pull( ) function ofcommunication 590 a. In response to communication 590 b, dialerapplication 520 instructs microphone application 550 to set themicrophone volume to 37 via a SetMicVol( ) function communicated incommunication 590 c.

In some scenarios, learning model 530 can communicate to an application,such as dialer application 520, once the learning model has been trainedand is ready to perform machine-learning operations, such as predictingmicrophone volume levels. In other scenarios, learning model 530 is nottrained sufficiently to provide a prediction in response tocommunication 590 a. In these scenarios, learning model 530 can informthe application that the learning model is insufficiently trained toperform machine-learning operations, such as predicting microphonevolume settings. In still other scenarios, microphone application 550can provide a response, such as a function return value or communication(e.g., “SetMicVolOK” or “SetMicVolFail”) to the SetMicVol( ) command.

FIG. 5B shows scenario 500 continuing by dialer application 520 sendingcommunications 592 and 594 a to learning model 530. Communication 592informs learning model 530 that a call is being made to a called party“Grandpa” and communication 594 a requests a predicted value for themicrophone volume setting. In response, learning model 530 predicts amicrophone volume setting of 81 based on data stored in the DialerModelvariable and learned from communications 564-586, particularlycommunications 584 and 586 which relate to a call to a called party of“Grandpa.” Subsequently, learning model 530 can send a Pull Response ascommunication 594 b to provide the predicted microphone volume settingof 81 to dialer application 520. In response to communication 594 b,dialer application 520 can instruct microphone application viacommunication 594 c to set the microphone volume to 81.

Scenario 500 concludes with dialer application 520 call Save( ) functionvia communication 596 to request that learning model 530 save a context,and perhaps other data, of a learning session thread in the DialerModelvariable. In some scenarios not shown in FIG. 5B, learning model 530 canprovide a response to communication 596, such as a return value to theSave( ) function and/or a communication providing a status of the Save() function; e.g., SaveOK or SaveFail.

FIG. 6A shows example user interface 610 used in scenario 600, inaccordance with an example embodiment. Scenario 600 includes userinterface 610 executing on mobile platform 602. As shown in FIG. 6A,user interface 610 includes microphone dialog 622 as an interface formicrophone application 620. Microphone dialog 622 includes manualmicrophone setting 624, smart microphone setting 626, an OK button tosave current settings before exiting dialog 622, and a Cancel button toexit dialog 622 without saving current settings.

Manual microphone setting 624 includes a slider bar that enables a userto set a microphone output setting to between a minimum setting 0, whicheffectively mutes the microphone, and a maximum setting of 100, whichprovides the maximum output volume (a.k.a. maximum gain) for a giveninput. FIG. 6A shows manual microphone setting 624 set to 48, orapproximately halfway between the minimum and maximum settings.

As shown in FIG. 6A, smart microphone setting 626 can be set to eitherenabled or disabled and used to learn and set microphone volume based oncalled party. When smart microphone setting 626 is disabled, microphoneapplication 620 can use manual microphone setting 624 to determine anoutput volume for a microphone of mobile platform 602. When smartmicrophone setting 626 is enabled, microphone application 620 can use alearning service of machine learning and adaptation service 220 toperform a machine-learning operation to provide setting values. Uponreceiving a setting value, microphone application 620 can determineoutput volume for the microphone of mobile platform 602. In scenario600, and as shown in FIG. 6A, smart microphone setting 626 is set toenabled.

FIG. 6B shows example communications for scenario 600, in accordancewith an example embodiment. Scenario 600 has similar communications tothose of scenario 500 shown in FIG. 5B, but there are two substantivedifferences. In scenario 500, dialer application 520 has access tocalling party information. In contrast, microphone application 620 doesnot have access to calling party information during scenario 600; thus,calling party information is hidden from microphone application 620 bylearning model 630. The second substantive difference is that inscenario 500, learning model 530 was trained in part using data storedin a DialerModel variable. In scenario 600, no stored model data isloaded into learning model 630.

In FIG. 6B, communications in scenario 600 begin with microphoneapplication 620 calling the getService( ) function of learning model 630via communication 652. In response, learning model 630 provides asession key S2 via communication 654 to microphone application 620.Session key S2 is included for subsequent communications betweenmicrophone application 620 and learning model 630 to permit addressingthe subsequent communications to the correct learning model, e.g.,learning model 630, and the correct application, e.g., microphoneapplication 620, for a learning session keyed by S2.

Microphone application 620 can instruct learning model 630, viacommunication 656, to set up a prediction interface. As shown in FIG.6B, communication 658 includes a PredictionInterface( ) call with threeparameters: session key 51, an input-source reference MicVol, and arequested output prediction of microphone volumes, as shown in FIG. 6B,using the predefined MIC_VOL value. The requested output prediction ofmicrophone volumes is based on called parties, as indicated by thefourth parameter CALLED_PARTY to the Predictionlnterface( ) function. Aswith the MIC_VOL value, the value of CALLED_PARTY can be predefined. Inother scenarios not shown in the Figures, the output prediction can bebased on multiple values as requested via the Predictionlnterface( )function.

In response, learning model 630 sends communication 658 to built-infeatures (BIFs) 640 to request built-in feature (ReqBI) of called partyvalues, as shown in FIG. 6B by passing the predefined CALLED_PARTY valueto built-in features 640. Built-in features 640 can include at least thefunctionality of built-in features 410 discussed above. In response,built-in features 640 returns a built-in session key BI2 to learningmodel 630 via communication 660. Built-in session key BI2 is includedwith subsequent communications between learning model 630 and built-infeatures 640 to permit addressing the subsequent built-in-featurerelated communications to the correct learning model.

FIG. 6B continues with microphone application 620 sending communication662 to learning model 630, via the Push( ) function, to indicate thatthe microphone volume is being set to a setting of 80. Additionally,built-in features 640 can send communication 664, via the Push( )function, to learning model 630 to indicate that a call is being made toa called party “Grandma.”

FIG. 6B shows a number of communications 666-678 for five subsequentcalls made with the calling parties indicated by built-in features 640and microphone volume indicated by microphone application 620 tolearning model 630 with the values listed in Table 4 below.

TABLE 4 Communications Called Party Microphone Volume 666 and 668 Boss40 666 and 670 Spouse 40 666 and 672 Boss 40 674 and 676 Grandma 82 674and 678 Grandpa 82

Scenario 600 continues with learning model 630 receiving communication680 from built-in features 640. Communication 680 informs learning model630 that a call is being made to a called party “Boss.”

In response to communication 680, learning model 630 predicts amicrophone volume setting of 40 based on data learned fromcommunications 662-678, particularly communications 666, 668, and 672which relate to calls to a called party of “Boss.” Subsequently,learning model 630 can send communication 682 including the Notify( )function to indicate the predicted microphone volume setting of 40 tomicrophone application 620. In response to communication 682, microphoneapplication 620 can perform function 684 to set the microphone volume to40.

Scenario 600 continues with learning model 630 receiving communication686 from built-in features 640, which informs learning model 630 that acall is being made to a called party “Grandpa.” In response tocommunication 686, learning model 630 predicts a microphone volumesetting of 82 based on data learned from communications 662-678,particularly communications 674 and 678 which relate to a call to acalled party of “Grandpa.” Subsequently, learning model 630 can sendcommunication 688 including the Notify( ) function to indicate thepredicted microphone volume setting of 82 to microphone application 620.In response to communication 688, microphone application 620 can performfunction 690 to set the microphone volume to 40.

Example Communication Action Prediction Service

FIG. 7 is a flow chart of method 700, in accordance with an exampleembodiment. In some embodiments, part or all of method 700 can beexecuted using one or more mobile platforms; e.g., mobile platform 200,502, 602, 802, 1202, 1502, 1852, 2002 and/or one or more computingdevices; e.g., computing device 2200.

Method 700 begins at block 710, where a context-identification systemexecuting on a mobile platform can receive data. The received data caninclude context-related data associated with the mobile platform andapplication-related data received from the mobile platform. In someinstances, the context-related data and the application-related data candiffer.

In some embodiments, context-related data can be received from anetwork. Further, in some instances, the network can include a server, acloud-based server, and/or a database with context-related data storeddynamically, among other possibilities to provide context-related data.Further, in some instances a mobile platform can connect to such anetwork to receive context-related data continuously and/orperiodically, possibly by executing a network connection application.Yet further, in some instances, context-related data can be received bya network with multiple programmable devices sitting on different nodesof the network, possibly as described with respect to FIG. 21. In suchinstances, context-related data can be received through the network frommultiple other programmable devices communicating with the network.

In addition, in some embodiments, the context-related data can bereceived and/or generated by a context-identification system (CIS)executing on an exemplary mobile platform. In particular embodiments,the CIS can be configured to extract context-related data frominformation streaming to the mobile platform, possibly from a network asdescribed above. In practice, the CIS may be initiated by acontext-related application executed on the mobile platform. Anexemplary context-identification system is further described withrespect to FIG. 9A.

In some embodiments, a context-related application may be executed tocause the CIS to stream context-related data associated with mobileplatform and identify a context. For example, the mobile platform may beturned off before a flight to Chicago and then turned back on after theflight arriving in Chicago. After turning on the mobile platform, thecontext-related application may cause the CIS to receive and/or generatecontext-related data. Further certain context signals associated withthe context-related data may be extracted such as: a) the location ofthe mobile platform (i.e. Chicago O'Hare International Airport), b) thetime zone based on the location of the mobile platform (i.e., CentralTime zone), and/or c) the weather of the area near the mobile platform(e.g., rainy at 58° F.), among other possibilities. In some instances,such context signals may be used to determine that the user has justarrived at Chicago O'Hare International airport via an airplane flight.

Yet further, in some embodiments, context-related data can be directlyreceived and/or generated by sensors associated with the mobile platformand/or components provided inside the mobile platform. In someinstances, sensors can be external to the mobile platform and capable ofreceiving and/or generating context-related data. By way of example andwithout limitation, sensors could be any one or more of a motiondetector (e.g., a gyroscope, an accelerometer, and/or a shock sensor), acamera, a proximity sensor, an impact sensor, a contact sensor (e.g.,capacitive sensing device), a location determination device (e.g., a GPSdevice), a magnetometer, and/or an orientation sensor (e.g., atheodolite), among other possibilities.

In some embodiments, application-related data can include data relatedto an exemplary mobile platform. For example, application-related datacan include data associated with operating the mobile platform. In someinstances, application-related data can include a “dialing indication”for opening a phone dialing application and/or a “received callindication” indicating a new call has been received at the mobileplatform. Further, a user can enter one or more digits of a phone numberto be dialed using the phone dialing application (e.g., entering thedigit “5” for the number “555-555-5555”). In some instances, receivedcall indications can include digits of a phone number from another phonecalling the mobile platform and/or a phone number from another phonethat has called the mobile platform in the past. In addition, anindication can include a digit of the mobile platform's phone number.Further, in some instances, a user can vocalize a command while usingvoice recognition to dial the phone number (e.g., verbally reciting“call my wife” in the vicinity of the mobile platform).

In some embodiments, application-related data can include a “messagingindication” for opening a messaging application, a short message service(SMS) text messaging application for example. Further, a user may enterone or more text characters of a message to be sent using the messagingapplication (e.g., entering “h” or “hey” for “hey, you want to play golftoday?”). The messaging application may receive a messaging indicationfor an incoming text message with incoming text (e.g., “Golf soundsgreat—which course?”). Yet further, a user can vocalize a command and/orpart of a command while using voice recognition to send a message usingthe phone messaging application (e.g., verbally reciting part of “hey,you want to play golf today?”) In some instances, a “contact indication”for opening a contact may be provided through a user interface of themobile platform. In other instances, an “application indication” forexecuting an application on the mobile platform may be provided throughthe user interface of the mobile platform.

Further, in some embodiments application-related data may include how anapplication is executed, opened, and/or interacted with. For example, insome instances, an application may be opened quickly, in parallel withother applications (e.g., a phone dialing application), and in someinstances, an application may be opened sequentially after performingother operations (e.g., opening other applications) with the mobileplatform. Yet further, in some instances, an application may be executedremotely from a mobile platform, perhaps using a laptop and/or a tabletcomputer. Other examples are possible as well.

At block 720, the context-identification system may identify at leastone context using the context-related data associated with the mobileplatform and/or the application-related data received from the mobileplatform.

Generally, identifying a context may be identified based oncontext-related data. Further, in some embodiments, context-related datamay include one or more “context signals.” Accordingly, an exemplarymobile platform may be configured to identify a context by determiningvarious context signals and/or acquiring context signals from othersources, such as the external sensors and/or networks, perhaps asmentioned above.

A context signal may be any signal that provides a measurement of orotherwise provides information pertaining to the state or theenvironment associated with a certain subject (e.g., with a certainperson, device, event, etc.). Many types of information from manydifferent sources may be used to establish context signals and/orprovide information from which context signals may be determined. Insome instances a context-identification system (CIS) may be used toreceive and/or generate one or more context signals.

By way of example and without limitation, context signals may include:(a) the current time, (b) the current date, (c) the current day of theweek, (d) the current month, (e) the current season, (f) a time of afuture event or future user-context, (g) a date of a future event orfuture user-based context, (h) a day of the week of a future event orfuture context, (i) a month of a future event or future user-context,(j) a season of a future event or future user-based context, (k) a timeof a past event or past user-based context, (l) a date of a past eventor past user-based context, (m) a day of the week of a past event orpast user-based context, (n) a month of a past event or past user-basedcontext, (o) a season of a past event or past user-based context,ambient temperature near the user (or near a monitoring deviceassociated with a user), (p) a current, future, and/or past weatherforecast at or near a current location, possibly based on the locationof the mobile platform, (q) a current, future, and/or past weatherforecast at or near a location of a planned event in which a user and/ora user's friends plan to participate, (r) a current, future, and/or pastweather forecast at or near a location of a previous event in which auser and/or a user's friends participated, (s) information on user'scalendar, such as information regarding events or statuses of a user ora user's friends, (t) information accessible via a user's socialnetworking account, such as information relating a user's status,statuses of a user's friends in a social network group, a user'srelationship with the user's friends, and/or communications between theuser and the user's friends, (u) noise level or any recognizable soundsdetected by the mobile platform and/or a monitoring device (v) itemsthat are currently detected by the mobile platform and/or a monitoringdevice, (w) items that have been detected in the past by the monitoringdevice, (x) items that other devices associated with a monitoring device(e.g., a “trusted” monitoring device) are currently monitoring or havemonitored in the past, (y) information derived from cross-referencingany two or more of: information on a user's calendar, informationavailable via a user's social networking account, and/or other contextsignals or sources of context information, (z) health statistics orcharacterizations of a user's current health (e.g., whether a user has afever or whether a user just woke up from being asleep), and (aa) auser's recent context as determined from sensors on or near the userand/or other sources of context information (e.g., whether the user iswalking, running, and/or jogging, among other possibilities), (bb) acurrent location of the user and/or the mobile platform, (cc) a pastlocation of the user and/or the mobile platform, and (dd) a futurelocation of the user and/or the mobile platform. Those skilled in theart will understand that the above list of possible context signalsand/or sources of context information is not intended to be limiting,and that other context signals and/or sources of context information arepossible in addition, or in the alternative, to those listed above.

Some context signals may take the form of discrete measurements. Forexample, a temperature measurement or a current GPS location may bereceived as a context signal. On the other hand, context signals mayalso be received over time, or may even be a continuous signal stream.For instance, an exemplary mobile platform may use the current volume ofa continuous audio feed from an ambient microphone as one contextsignal, and the volume of a continuous audio feed from a directionalmicrophone as another context signal.

In some embodiments, a context may be identified by extracting and/orinterpreting context signals from other types of data (e.g., weatherforecast data, satellite data, GPS coordinates, etc.). Further, in someembodiments, a machine-learning operation may cluster the one or morecontext signals by grouping one or more context signals. In addition, amachine-learning operation may categorize or classify the context byassigning a label to one or more context-signals, possibly aftergrouping them. For example, one or more context signals may take theform of data indicative of the environment or state information. Assuch, context signals may be categorized or classified as “at home,” “atwork,” “outside,” “in a car,” “outdoors,” “indoors,” “inside,”“outside,” “free,” and/or “in a meeting,” among other possibilities.

Furthermore, a context may be a qualitative or quantitative indicationthat is based on one or more context signals. In addition, a context maybe identified by extracting specific context signals from an aggregationof context signals. For example, context signals may indicate a changein time to 6:30 AM on a weekday (possibly setting off an alarm set by amobile platform). The user may be located at their home and suchinformation may be used to categorize or classify contexts such that theuser went from “sleeping” to “awake.” In some instances, a context maysimply be reflected in a context-related database as “getting ready forwork.”

In some embodiments, a context may be identified by a mobile platform.For example, a mobile platform may determine, based on GPS locationsignals, that it has changed its location from one city to anotherdifferent city. As such, the mobile platform may determine the contextto be changing locations from “in Los Angeles” to “in Chicago.” Manyother examples are also possible. In yet another example, a context mayinclude data indicating changes to the environment or state informationsuch as moving from “home” to “at work,” from “outside” to “in a car,”from “outdoors” to “indoors,” from “inside” to “outside,” and/or from“free” to “in a meeting,” among other possibilities. Further, in someinstances, a context may indicate “going to work,” “getting in the car,”“going inside,” “going outside,” and/or “going to a meeting,” amongother possibilities.

Further, in addition to and/or alternatively, the context-identificationsystem may identify a context based on application-related data. Forexample, a user operating an exemplary mobile platform may provide anindication of a current context. In particular, after receiving a textmessage via the mobile platform's text messaging application, the usermay respond in a text message reciting, “Can't talk, in a meeting.” In afurther example, the user may update their status on a social networkingsite by stating, “I am so excited about this all-day meeting.” Inaddition, the user may have accepted a meeting invite to the all-daymeeting set in the user's calendar such that the context-identificationcan identify the context of “attending a meeting.”

Yet further, a context may be identified by a user indication. Forexample, a dialing indication may be generated by opening up a phonedialing application on the mobile platform. In some instances, a dialingindication can be received by the phone dialing application, e.g., adialing indication for an incoming phone call. In such instances, thedialing indication may be used to identify a context for “callingco-worker.” Further, entering the first digit of the co-worker's phonenumber may further provide an indication for the “calling co-worker”context.

Yet additionally, a messaging indication may be generated by opening upthe messaging application on the mobile platform. In some instances, amessaging indication can be received by the messaging application, e.g.,a messaging indication for an incoming text message. In such instances,the messaging indication may be used to identify a context for“messaging co-worker.” Further, selecting and/or opening up themessaging co-worker's contact may further provide an indication for suchcontexts directed to communicating with the messaging co-worker. Otherexamples are possible as well.

At block 730, based on at least one context identified, thecontext-identification system can predict at least one communicativeaction associated with the mobile platform by performing amachine-learning operation on the received data.

In some embodiments, the user may manually provide the context byentering the text “going to office” in a context field of the mobileplatform's user interface. In addition, as noted, a context may beclassified by assigning a label to one or more context-signals. In someembodiments, a machine-learning operation may be performed to assignsuch labels. For example, the mobile platform may determine the contextfrom “subway” to “office” when the user leaves the subway station andarrives to their office.

Further, the context identified may provide an indication of acommunicative action. By way of example and without limitation, a“communicative action” may include any of the following: dialing a phonenumber using the mobile platform, ending a current call using the mobileplatform, sending a message using the mobile platform, opening a contacton the mobile platform, executing an application on the mobile platform,updating a status on social networking site, and/or sendingcommunication via email, among other possibilities.

In some embodiments, identifying a context may help predict at least onecommunicative action. For instance, an exemplary mobile platform maystore context signals associated with “leaving for work,” “driving towork,” “driving home from work,” and “arriving home.” Further, themobile platform may track previously-called phone numbers associatedwith such contexts, possibly storing the previously-called phone numberswith their respective contexts in a context-related database. Forexample, if the mobile platform identifies the “leaving for work”context, the mobile platform may display (e.g., on its user interface) aphone-number for the “leaving for work” context taken from a list of allpreviously-stored phone numbers called associated with the “leaving forwork” context. Other possibilities may also exist.

In some embodiments, there may be more than one phone number associatedwith a given context identified. In such instances, a machine-learningoperation may be performed to rank the phone numbers associated with thegiven context. For example, the mobile platform may have stored threephone numbers associated with the “driving home from work” context: 1) ason's phone number, 2) a daughter's phone number, and 3) a wife's phonenumber. In some instances, the mobile platform may be able to determineits velocity, possibly consistent with the movement of the user's car.For example, the mobile platform may be able to detect if it is movingtoward the son's location, the daughter's location, and/or the wife'slocation.

Based on the location and/or velocity of the mobile platform, the mobileplatform may change the rankings of the three phone numbers stored. Inthis example, the mobile platform's location and velocity may indicatethe mobile platform is traveling toward the son's location. Given themobile platform's current direction toward the son's location andpossibly as the mobile phone's location gets closer to the son'slocation, the mobile platform may suggest the user call the son, perhapsby displaying the son's phone number as a “Suggested Call” and/or“Suggested Number” on the phone dialing application's user interface.

Further, identifying changes in context may allow a mobile platform tointelligently predict communicative actions (e.g., a phone number todial). In some instances, the mobile platform may determine that acurrent context matches a context that corresponds to apreviously-dialed phone number. In such instances, the mobile platformmay suggest the user to call the previously-dialed phone number. Thisaspect is discussed in greater detail below with reference to FIG. 8B.Other examples are possible as well.

At block 740, an instruction can be received to execute at least onecommunicative action associated with the mobile platform.

In some embodiments, the instruction may be provided from a user of themobile platform. For example, the mobile platform may provide a prompton its user interface with a suggested number to call. In someinstances, the suggested number to call may be provided based on aprediction using the machine-learning operation. Further, the user mayuse an instruction input on the mobile platform's user interface toinitiate the call to the suggested number.

Further, in another example, the mobile platform may identify the“leaving work and going home” context as the user is driving their carfrom the workplace after a day at work. In such instances, the mobileplatform may prompt the user with a computerized voice communicationstating, “Would you like to text your wife that you are ‘leaving workand going home’?” Utilizing voice recognition capabilities, the user mayrespond, “Yes,” and a text message may be sent to the user's wifeindicating that the user is “leaving work and going home.” As such, theuser is able to send text messages without making physical contact withthe phone and/or making visual contact with the phone, possibly torefrain from distracting the user from driving.

In some embodiments, the instruction may be provided from thecontext-identification system (CIS). For example, the CIS may identify acontext associated with “arriving to the office” and provide aninstruction to dial into a conference call as done regularly uponidentifying the “arriving to the office” context. In some instances,previously-stored data may indicate a pre-determined instruction to dialinto the conference call upon identifying the “arriving to the office”context. In other scenarios, pre-determined instructions can enable theCIS to automatically originate other phone calls, generate and send textmessages, and/or other communicative actions, Many other examples arepossible as well.

FIG. 8A shows an example user interface 804 used in scenario 800, inaccordance with an example embodiment. Scenario 800 includes userinterface 804 executing on mobile platform 802. User interface 804includes an interface for context identification 806. Further, userinterface 804 includes an interface for suggested contact 808 to beviewed, possibly based on context identification 806. In addition, userinterface 804 includes an interface for suggested phone number 810 to bedialed, also possibly based on context identification 806. Further, userinterface 804 includes instruction input 812.

Further, FIG. 8A shows context identification 806, perhaps based onreceiving context related-data. For example, mobile platform 802 mayreceive multiple context signals such as the date, time, temperature,and/or weather, as shown by context-related data 814.

Context identification 806 may illustrate the identified context, “Mar.12, 2012 8:05 AM Chicago O'Hare Airport Arrival,” by accessing apre-stored entry in the user's calendar indicating when the user isscheduled to land in Chicago. Yet, in some instances, mobile platform802 may detect a recent operation for turning on the power of mobileplatform 802 and use a GPS locator to detect the location of mobileplatform 802. In such instances, mobile platform 802 may make aprediction that the user has arrived at Chicago O'Hare Airport, perhapsafter a flight. In other instances, mobile platform 802 may access theuser's itinerary stored in mobile platform 802's memory to identify thatthe user has arrived at Chicago O'Hare Airport after a flight. Otherpossibilities may exist.

FIG. 8A shows context identification 806 with a “Change” button tochange and/or correct the context identified, a “Save” button to savethe context identified, and a “Dismiss” button to dismiss the contextidentified without saving the context identified. In some instances, the“Change” button may allow the user to correct the context identified toprovide further context-related data, possibly to predict communicativeactions. In some instances, the “Save” button may be used foridentifying contexts in the future, and perhaps storing the contextsignals associated with the context identified.

In addition, FIG. 8A shows suggested contact 808 as “Limo Driver.” Insuch instances, the user may press the “Limo Driver” button to seefurther information regarding the Limo Driver and possibly view otheroptions for text messaging the Limo Driver. In some embodiments,multiple suggestions can be provided, and the user can press the circleenclosing a downward-facing triangle within suggested contact 808 toview additional suggestions. For example, pressing the circle mayprovide a list of other contacts, possibly including local taxiservices, rental car services, airport bus services, and/or othercontacts that the user may want view and/or call based on the contextidentified. Other possibilities may also exist.

FIG. 8A shows suggested phone number 810 as “555-555-5555” as the phonenumber to call the Limo Driver. In some examples, the user may providean instruction through instruction input 812. Instruction input 812includes a Wait button to possibly delay the phone call to the LimoDriver such that the call may be made later upon a timed prompt (notshown in FIG. 8A) for suggested phone number 810. Instruction input 812also provides a Dial button to call the Limo Driver immediately and aCancel button to exit user interface 804 without calling the LimoDriver.

FIG. 8B shows an example airport arrival data record 820 based onreceived context-related data 822. It should be understood that airportarrival data record 820 may correspond to scenario 800 in FIG. 8A.Airport arrival data record 820 may be stored in mobile platform 802,perhaps in mobile platform 802's memory. Further, airport arrival datarecord 820 may be stored on a server, a network, cloud-based system,and/or other server system configured to store data records withcontext-related data, among other possibilities. In some embodiments,data records with context-related data may be updated by multiple mobileplatforms and/or computing devices, possibly if authorized by a user ofmobile platform 802.

In some embodiments, airport arrival data record 820 may includecontext-related data 822 corresponding to the date, time, temperature,and/or weather, as shown by context-related data 814 in FIG. 8A.Further, in FIG. 8A, consider the scenario such that the “save” buttonis pressed in context identification 806. Referring back to FIG. 8B,context-related data 822 may be updated to include additional datacorresponding to a recent operation for turning on the power of mobileplatform 802 and the GPS location data of mobile platform 802 indicativeof the arrival at Chicago O'Hare Airport after a flight.

As part of airport arrival data record 820, FIG. 8B further illustrateslimo driver data 824 with its corresponding phone number data 826,contact data 828, and context-related data 830. In addition, user's sondata 832 may include its corresponding phone number data 834, contactdata 836, and context-related data 838. Further, user's daughter data840 may include its corresponding phone number data 842, contact data844, and context-related data 846. Yet further, user's wife data 848 mayinclude its corresponding phone number data 850, contact data 852, andcontext-related data 854.

In some embodiments, context-related data 822, 830, 838, 846, and 854may be stored in airport arrival data record 820 to predict acommunicative action by a user of mobile platform 802. As such,context-related data may include mobile platform 802's call history(e.g., contacts called most frequently), information corresponding tothe user's relationships with other contacts (e.g., people that the userhas spoken to the most over a given period in time), and/or browsinghistory of an internet browser application indicative of the user'sinterests. Other possibilities may also exist.

In FIG. 8B, phone number data 826, 834, 842, and 850 may includeinformation regarding each respective person's phone number. Further,phone number data 826, 834, 842, and 850 may also include more than onephone number for a given person if the person has more than one phonenumber (e.g., cell phone number, office number, and/or pager number,among other possibilities). For example, phone number data 826 maycorrespond to the limo driver's phone number, 555-555-5555, shown assuggested phone number 810 in FIG. 8A. Further, contact data 828, 836,844, and 852 may correspond to each person's availability, possiblyincorporating work schedules, historical data indicative of when eachperson has answered (or has not answered) after calling them duringcertain times, and/or other possible alternative contacts to reach theperson, among other possibilities.

Further, in FIG. 8B, context-related data 830, 838, 846, and 854 maycorrespond to context signals detected when each respective person hasbeen called by mobile platform 802 and/or when each person has calledmobile platform 802. For example, context-related data 830 maycorrespond to context signals indicating an operation for turning on thepower of mobile platform 802 and the GPS location data of mobileplatform 802 indicative of an arrival at Chicago O'Hare Airport after aflight. Context-related data 838, 846, and 854 may also correspond tocontext signals indicative of the GPS location at Chicago O'HareAirport. However, context-related data 838, 846, and 854 may not includedata suggestive of a recent operation turning on the power of mobileplatform 802 indicative of an after-flight arrival. For example, it maybe possible that the son, the daughter, and the wife were previouslycalled just prior to the user's departure from Chicago O'Hare Airport.

A current context identified with mobile platform 802 may closely matchcontext-related data 822. Further, context-related data 822 may matchmost closely with context-related data 830 as compared tocontext-related data 838, 846, and 854. As such, limo driver data 824may be ranked higher than user's son data 832, user's daughter data 840,and user's wife data 848, indicating that the user is more likely tocall the Limo Driver. Thus, a prediction can be made that the user willwant to contact the Limo Driver using phone number data 826.

In some embodiments, contact data 828, 836, 844, and 852 may establish aweighting factor to further predict who the user may want to call. Forexample, contact data 828 may indicate that the limo service does notopen until 9:00 AM on Mar. 12, 2012. Thus, although context-related data822 may match most closely with context-related data 830, limo driverdata 824 may fall in the rankings since the limo driver is probably notavailable. In such instances, user's son data 832 may be next in line toprompt the user with a suggested phone number corresponding to phonenumber data 834.

In some embodiments, application-related data corresponding to theuser's indications on the mobile platform may adjust the weightingfactor. For example, as noted in FIG. 8A, instruction input 812 providesa Cancel button to not call the Limo Driver at all. Provided that theuser presses the Cancel button, a weighting factor may be applied tolower limo driver data 824 in the rankings, perhaps eliminating limodriver data 824 from the rankings and/or eliminating limo driver data824 from airport arrival data record 820. Other examples are possible aswell.

FIG. 9A shows example context identification system (CIS) 900, inaccordance with an example embodiment. CIS 900 receives context signals910. In some instances, context signals 910 can be stored as a datastring in a mobile platform, a context-related database, a cloud, and/ora network, among other possibilities.

Context identification 912 of CIS 900 can receive context signals 910,such as, but not limited to (a) a current time, (b) a current date, (c)a current day of the week, (d) a current month, (e) a current season,(f) a time of a future event or future context, (g) a date of a futureevent or future context, (h) a day of the week of a future event orfuture context, (i) a month of a future event or future user-context,(j) a season of a future event or future context, (k) a time of a pastevent or past context, (l) a date of a past event or past context, (m) aday of the week of a past event or past context, (n) a month of a pastevent or past context, (o) a season of a past event or past context, (p)ambient temperature, (q) a current, future, or past weather forecast ata current location, (r) a current, future, or past weather forecast at alocation of a planned event, (s) a current, future, or past weatherforecast at or near a location of a previous event, (t) information on acalendar associated with a user-profile, (u) information accessible viaa user's social networking account, (v) noise level or any recognizablesounds detected by a device, (w) devices that are currently available tocommunicate with the mobile platform, (x) devices in proximity to themobile platform, (y) devices that are available to receive instructionfrom the mobile platform, (z) information derived from cross-referencingat least one of: information on the user's calendar, information sent tothe user, and/or information available via the user's social networkingaccount, (aa) health statistics or characterizations of the user'scurrent health, (bb) a user's recent context as determined from sensorson or near the user and/or other sources of context informationassociated with the mobile platform, (cc) a current location of themobile platform, (dd) a past location of the mobile platform, and (ee) afuture location of the mobile platform.

For some or all of the context signals received, context identification912 can copy and/or extract data from context signals 910 to identifyone or more contexts. The context identified can be output from contextidentification 912 as a vector.

Classifier function 914 of CIS 900 can receive the context vector asinput, classify contexts into a CC context classification, CC>0, andoutput classification indications. For an example with CC=3, a contextmay be classified as “work,” “home,” and “travel” based on an activity,a state of the user, an arrival, a departure, context signal(s), etc.Many other classifications are possible as well.

A classification indication can include the classification of a context,possibly using a predetermined classification scheme. For example,suppose classifier function 914 determines a recurring contextindicative of the user going on their lunch break at noon. In thisexample, classifier function 914 can output a classification indicationassigning a label “going out to lunch.” Many other possibleclassification indications are possible as well.

Recognition function 916 can receive the classification indications andrecognize certain functions. For example, upon receiving theclassification indication of the mobile platform “moving towards thepizza parlor,” recognition function 916 can recognize previous phonecalls made in the context of “moving towards the pizza parlor.” Forexample, in some prior instances, perhaps the user made a call to thepizza parlor to make reservations. Then, recognition function 916 canrecognize these previous phone calls and generate a predictedcommunication (e.g., phone call, e-mail or text message) to the pizzaparlor.

Recognition function 916 can be performed using software and data solelyresident and executing on a mobile platform, while in other embodiments,recognition function 916 can be performed using software and data bothresident and executing on the mobile platform and on other computingdevices, such as one or more servers configured to recognize contexts.

Application-related data 918 can include data generated by and/orrelated to an application executed on the mobile platform. Examples ofapplication-related data 818 can include, but are not limited tointeracting with applications, initiating programs on the mobileplatform, and/or executing communicative actions such as dialing phonenumbers, among other possibilities. Additional examples ofapplication-related data are discussed above in the context of FIG. 7,among other figures as well. Further, application-related data 918 candiffer from context-related data, context vector(s) extracted by contextidentification 912, classification indications from classifier function914, and recognized contexts from recognition function 916.

In some instances, user model 920 can take recognized contexts fromrecognition function 916 and application-related data 918 to predictand/or suggest communicative actions, such as possible phone numbers todial. For example, suppose that the mobile platform recognizes a contextsuch that the user is on their lunch break and is at their officecontinuing to work. Further, application-related data may indicate thatthe user is initiating a phone dialing application and/or a restaurantbrowsing application. The mobile phone may predict that the user maywant to order food and provide a phone number to a local restaurant,possibly a previously-called restaurant for making such a food order.

FIG. 9B shows an example collective context identification system (CCIS)902, in accordance with an example embodiment. CCIS 902 can beconfigured to receive context-related data and application-related datafrom a number N of mobile platforms. In some embodiments, each of the Nmobile platforms can be configured to determine whether or not toprovide some or all context-related data and/or application-related datato CCIS 902. In other embodiments, some or all of the context-relateddata and/or application-related data used CCIS 902 can be anonymouslyused.

As shown in FIG. 9B, context-related data, including context signals 922a, 922 b, 922 c and recognized contexts 924 a, 924 b, 924 c, andapplication-related data 918 a-918 c can be provided to collectivelearner 928. Collective model 930 can take recognized contexts providedby collective learner 928 to suggest communicative actions to execute,such as possible phone numbers to dial and/or addresses for textmessages and/or emails to be sent.

FIG. 9C shows an example feedback-adaptable context identificationsystem (FACIS) 904, in accordance with an example embodiment. FACIS 904takes application-related data 918 and context-related data 922 asinputs, and provides the inputs to both user model 920 and collectivemodel 930, which respectively generate user-specific communicationaddresses 940 and general communication addresses 942. For example,user-specific communication addresses 940 may include phone numbers,e-mail addresses, Instant Messaging (IM) addresses, IP addresses,Uniform Resource Locators (URL)s, and/or other communication addressesto restaurants previously contacted by a user whereas generalcommunication addresses 942 may include phone numbers, e-mail addresses,Instant Messaging (IM) addresses, IP addresses, Uniform ResourceLocators (URL)s, and/or other communication addresses to commonrestaurants, and perhaps other establishments, in proximity to themobile platform's current location. User-specific communicationaddresses 940 and general communication addresses 942 can be provided tocommunication address suggestion user interface (UI) 944 forpresentation and possible selection as a communication address forcontacting one or more entities.

For example, a user using communication address suggestion UI 944 canview one or more suggested user-specific communication addresses 940,and perhaps provide additional feedback, such as dismissing thesuggestion (i.e. refusing to use the suggested communication address).Information about the suggested communication address and any additionalfeedback can be respectively provided as feedback 946, 948 to user model920 and collective model 930. Based on this feedback, user model 920 andcollective model 930 can adapt to the feedback such as by increasingprobabilities that the next communication address suggested by the othermodel will be accepted by the user. Continuing this example, user model920 can examine feedback 946, facilitate collective model 930'sprediction for another communication address to be used, and increasethe probability that the next communication address provided tocommunication address suggestion communication address UI) will beaccepted going forward. Additionally or instead, collective model 930can examine feedback 948, facilitate user model 920's prediction foranother communication address to be used, and increase the probabilitythat the next communication address provided to communication addresssuggestion user UI 944 will be accepted going forward.

Example Photo Name/Photo Album Suggestion Service

Mobile platforms, such as mobile phones, can include one or more camerasto capture images. Frequently, the images are named, perhaps with abrief description of the image. For example, an image named “Sunset 18Mar. 2012” can be a picture of a sunset captured on Mar. 18, 2012.Labeling a large number of pictures can take a great deal of time inreviewing the image, determining a title for the image, and then namingthe image with the determined title.

An image identification system (IIS), based on the learning modelsdescribed above, can suggest image and album titles based on imagecontent and other data. The other data can include social signals suchas social-networking messages, postings, comments, tags, e-mail andother messages, other persons' social signals, and social statusindicators, calendar data, locations, and times. The IIS can learn acollective model that represents the collective data from at least oneperson and their images. The collective model can be learned, stored,and executed on the mobile platform alone or, in some embodiments, onthe mobile platform and one or more other computing devices 2200, suchas servers.

The IIS can also be configured with image feature recognition anddetection capabilities, such as facial recognition modules, objectrecognition modules, landmark recognition models, and modules torecognize features in images such as lines, edges, patches of color,shapes, object areas/volumes, and other features. The IIS can detectfeatures in one image, a sequence of images, and/or a video file orstream for a sequence of one or more images.

The IIS and/or collective model can include or otherwise be associatedwith a personalized model, perhaps using data stored solely on themobile platform. The personalized model can be equipped to adapt tofeedback from a user of the mobile platform. For example, for a givenimage IMG1, the personalized model can suggest three titles: T1, T2, andT3. Then, if the user decides to entitle IMG1 with title T2, thepersonalized model can increase the probability that title T2 and/ortitles similar to title T2 are suggested in the future. In response toselection of title T2 in this example, the personalized model candecrease the probabilities that unselected titles T1 and T3 and/ortitles similar to title T2 are suggested in the future

FIG. 10 is a flow chart of method 1000, in accordance with an exampleembodiment. In some embodiments, part or all of method 1000 can beexecuted using one or more mobile platforms; e.g., mobile platform 200,502, 602, 802, 1202, 1502, 1852, 2002 and/or one or more computingdevices; e.g., computing device 2200.

Method 1000 begins at block 1010, where a machine-learning serviceexecuting on a mobile platform can receive feature-related data. Thefeature-related data can include image-related data related to one ormore images received from an application executing on the mobileplatform and platform-related data received from the mobile platform.The image-related data and the platform-related data can differ.

In some embodiments, the image-related data can be generated by animage-identification system (IIS) executing on the mobile platform. Inparticular embodiments, the IIS can be configured to extractimage-related features from the one or more images. In more particularembodiments, the IIS can be configured to classify at least oneimage-related feature of the image-related features. In even moreparticular embodiments, the IIS can be configured to recognize the atleast one classified image-related feature and/or object(s) and/orscene(s) that include the at least one classified image-related feature.

In some of the even more particular embodiments, the at least oneclassified image-related feature can include a feature related to a faceof a person, and the IIS can be configured to recognize the face of theperson. In other of the even more particular embodiments, the at leastone classified image-related feature can include a feature related to alandmark, and the IIS can be configured to recognize the landmark. Instill other of the even more particular embodiments, the at least oneclassified image-related feature can include a feature related to anobject, and the IIS can be configured to recognize the object.

At block 1020, the machine-learning service can generate a title relatedto the one or more images. The title can be generated by themachine-learning service performing a machine-learning operation on thefeature-related data.

In some embodiments, the title related to the one or more images caninclude at least one datum from the image-related data and at least onedatum from the platform-related data. In particular embodiments, the atleast one datum from the image-related data can include at least onedatum selected from among a name of a person, a name of a landmark,and/or a name of an object and the at least one datum from theplatform-related data includes at least one datum selected from among adate, a time, a calendar entry, a social-networking message, and alocation.

At block 1030, the machine-learning service can send the title relatedto the one or more images from the machine-learning service to theapplication.

FIG. 11A shows example image identification system (IIS) 1100, inaccordance with an example embodiment. IIS 1100 receives an input image1110. Image 1110 can be an electronically-storable image perhaps storedin a well-known image format, e.g., JPG, MPEG, GIF, BMP, etc. Featureextraction function 1112 of IIS 1100 can detect features from image1110, such as, but not limited to, lines/edges, corners, points ofinterest, contours, patches of color, regions of interest. For some orall of the detected features, feature extraction function 1112 can copyand/or extract a “local image patch” from image 1110 that includes thedetected feature. The detected feature(s) and/or corresponding localimage patch(es) can be output from feature extraction function 1112 as afeature vector.

Classifier function 1114 of IIS 1100 can receive the feature vector asinput, classify features into one of IC image classifications, IC>0, andoutput classification indications. For an example with IC=3, thefeatures can be classified as a face, a landmark, or an object. Manyother classifications are possible as well.

A classification indication can include the classification of arecognized feature, and an indication of a range of pixels that includethe classified feature. For example, suppose classifier function 1114determines an image of a face is depicted by a rectangle of pixels whoseupper-left-hand corner has pixel coordinates (10, 10), and whoselower-right-hand corner has pixel coordinates (90, 100). In thisexample, classifier function 1114 can output a classification indicationof <face, (10, 10), (90, 100)>, to indicate an image of a face can belocated within the rectangle having pixel coordinates from (10, 10) to(90, 100). As another example, a classification indication of <landmark,(1,100), (400, 400)> can indicate an image of a landmark can be foundwithin the rectangle having pixel coordinates from (1, 100) to (400,400). Many other possible classification indications are possible aswell.

Recognition function 1116 can receive the classification indications andrecognize the feature in the classification indication. For example,upon receiving the classification indication <face, (10, 10), (90,100)>, recognition function 1116 can attempt to recognize the face inthe image. Recognition function 1116 can be performed using software anddata solely resident and executing on a mobile platform, while in otherembodiments, recognition function 1116 can be performed using softwareand data both resident and executing on the mobile platform and on othercomputing devices, such as one or more servers configured to recognizeimage features.

Platform-related data 1118 can include data generated by and/or relatedto a mobile platform. Examples of platform-related data 1118 include,but are not limited to times, dates, locations, calendar entries,social-networking messages and other messages. Additional examples ofplatform-related data are discussed above as built-in feature-relateddata in the context of at least FIG. 2. Other examples are possible aswell. Feature-related data 1118 can differ from image-related data thatincludes image 1110, feature vector(s) extracted by feature extractionfunction 1112, classification indications from classifier function 1114,and recognized features from recognition function 1116.

User model 1120 can take recognized features from recognition function1116 and platform-related data 1118 to generate titles for images and/orcollections of images (a.k.a. albums). For example, suppose an image I1had three recognized features: recognized faces of Alice and Bob and arecognized object of a bottle, and that the platform-related dataindicated that I1 was taken at 9 AM on Mar. 18, 2012, at Alice and Bob'shome. Platform-related data 1118, authorized by Bob to examine hiscalendar entries, can include a calendar entry of “St. Pats Party 2012”that was held starting at 7 PM on Mar. 17, 2012. Based on thisinformation, user model 1120 can generate example titles for image I1,such as:

“Alice_and_Bob_(—)18Mar2012”

“Alice_and_Bob_home_(—)18Mar2012”

-   -   “Alice_and_Bob_home_with_bottle_(—)18Mar2012”    -   “Alice_and_Bob_home_with_bottle_after_St Pats Party 2012”.

Similarly, if images I2, I3, and I4 are taken in succession, all ofwhich include images of Alice, Bob, and the bottle, the above-generatedexample titles can be used for a photo album that includes images I1,I2, I3, and I4. Further, the above-generated example titles could beused for a video clip of Bob, Alice, and the bottle taken at 9 AM onMar. 18, 2012 that includes images I1, I2, I3, and I4. Images I1-I4 canreside on a mobile platform; e.g., Alice's mobile platform, and/or on aserver configured to store data, such as images I1-I4.

FIG. 11B shows an example collective image identification system (CIIS)1102, in accordance with an example embodiment. CIIS 1102 can beconfigured to receive image-related data and platform-related data froma number N of mobile platforms. In some embodiments, each of the Nmobile platforms can be configured to determine whether or not toprovide some or all image-related data and/or platform-related data toCIIS 1102. In other embodiments, some or all of the image-related dataand/or platform-related data used CIIS 1102 can be anonymized.

As shown in FIG. 11B, image-related data, including images 1122 a, 1122b, 1122 c and recognized features 1124 a, 1124 b, 1124 c, andplatform-related data 1118 a-1118 c can be provided to collectivelearner 1128. Collective model 1130 can take recognized featuresprovided by collective learner 1128 to generate titles for images and/orcollections of images (a.k.a. albums).

FIG. 11C shows an example feedback-adaptable image identification system(FAIIS) 1104, in accordance with an example embodiment. FAIIS 1104 takesplatform-related data 1118 and image-related data 1122, 1124 as inputs,provides the inputs to both user model 1120 and collective model 1130,which respectively generate user-specific titles 1140 and general titles1142. Image-related data 1122, 1124 can include images 1122 a-c and/orrecognized features 1124 a-c. User-specific titles 1140 and generaltitles 1142 can be provided to image/album title suggestion userinterface (UI) 1144 for presentation and possible selection as an image,video, or album title.

For example, a user using image/album title suggestion UI 1144 canselect one of user-specific titles 1140, and perhaps provide additionalfeedback, such as a rating of the title. Information about the selectedtitle and any additional feedback can be respectively provided asfeedback 1146, 1148 to user model 1120 and collective model 1130. Basedon this feedback, user model 1120 and collective model 1130 can adapt tothe feedback such as by increasing probabilities of titles generated bythe other model. Continuing this example, user model 1120 can examinefeedback 1146, determine that collective model 1130 generated theselected title, and increase the probability that titles similar to theselected title are provided to image/album title suggestion UI 1144going forward. Additionally or instead, collective model 1130 canexamine feedback 1148, determine that user model 1120 generated theselected title, and increase the probability that titles similar to theselected title are provided to image/album title suggestion UI 1144going forward.

FIG. 12A shows an example user interface 1210 used in scenario 1200, inaccordance with an example embodiment. Scenario 1200 includes userinterface 1210 executing on mobile platform 1202. User interface 1210includes an interface for camera application 1220 and photo album/titleapplication 1222. In scenario 1200, FIG. 12A shows photo album/titleapplication 1222 operating in the foreground and utilizing suggestedalbum/title dialog 1222 a. Suggested album/title dialog 1222 a includesalbum suggestion 1226 and title suggestion 1228.

FIG. 12A shows that album suggestion 1226 includes an album namesuggestion of “Chicago_May_(—)2012”, a Suggest button to request anotheralbum name suggestion, a Save button to create an album named assuggested, e.g., Chicago_May_(—)2012, an Edit button to modify the albumname, and a Cancel button to exit the photo album/title application 1222without creating an album title. FIG. 12A also shows title suggestion1228 with a title suggestion of “AnnJohn_ChicagoTheater_(—)23May2012.jpg” for a particular image, a Suggest button to request anothertitle suggestion, a Save button to save an image named as suggested,e.g., AnnJohn_ChicagoTheater_(—)23May 2012.jpg, an Edit button to modifythe album name, and a Cancel button to exit the photo album/titleapplication 1222 without using the suggested title.

In some embodiments, use of the Edit button to change album suggestion1228 and/or title suggestion 1228 can generate feedback 1146 to usermodel 1120 and/or feedback 1148 to collective model 1130. Feedback 1146,1148 can include the originally-suggested title and the title as edited.Feedback 1146, 1148 can be provided to the user model 1120 and/orcollective model 1130 in response to use of the Save button to save anedited album suggestion 1226 and/or title suggestion 1228.

In scenario 1200, image 1224 has recently been captured. Image 1224 isan image of two people, Ann and John, pictured beneath the marquee ofthe landmark Chicago Theater. Image 1224 has been provided to photoalbum/title application 1222, which in turn used an image identificationsystem, such as IIS 800, CIIS 802, or FAIIS 804, to generate thesuggested image title and image album names discussed above and shown inFIG. 12A.

In some embodiments not explicitly shown in the Figures, photoalbum/title application 1222 can operate in the background if soauthorized; e.g., photo album/title application 1222 operates withoutdisplaying suggested album/title dialog 1222 a. Then, when an image iscaptured, such as image 1224, album/title application 1222 can providethe captured image to photo album/title application 1222 and generatesuggested image title and image album names, perhaps after enough imageshave been captured and/or named to train learning model 1230. Once thesuggested image title and image album names are generated, album/titleapplication 1222 can generate a notification, such as a pop-up dialog,that presents the suggested image title and/or image album names.

Timing and/or display of the notifications can depend on user response.For example, album/title application 1222 can use threshold times todetermine “relatively quick” and “relatively slow” response. Forexample, if a user responds to a notification within a relatively-quickthreshold period of time; e.g., within five seconds of notificationdisplay, album/title application 1222 can continue to generatenotifications as soon as suggested image title and/or image album namesare available. As another example, if a user does not respond to anotification within a relatively-slow threshold period of time; e.g.,within sixty seconds of notification display, album/title application1222 can either generate notifications less frequently and/or stopgenerating notifications in favor of waiting for album/title application1222 to be returned to the foreground, and then provide suggested imagetitle and/or image album names via suggested album/title dialog 1222 a.Other values for threshold periods of time are possible.

FIG. 12B shows communications for the example scenario 1200, inaccordance with an example embodiment. Communications shown in FIG. 12Binvolve photo album/title application 1222 requesting ranked lists ofphoto titles and photo album names from learning model 1230. Learningmodel 1230 gets image-related data from IIS 1250 and platform-relateddata from built-in features (BIFs) 1240. Built-in features 1240 caninclude at least the functionality of built-in features 410 discussedabove. When photo album/title application 1222 provides an image tolearning model 1230, learning model 1230 generates a list of a photoalbum names and photo titles.

Photo album/title application 1222 initiates communications in scenario1200 by calling the getService( ) function for learning model 1230 viacommunication 1252. In response, learning model 1230 provides a sessionkey S3 via communication 1254 to photo album/title application 1222.Session key S3 is included for subsequent communications between photoalbum/title application 1222 and learning model 1230 to permitaddressing the subsequent communications to the correct learning model,e.g., learning model 1230, and to the correct application, e.g., photoalbum/title application 1222, for a learning session keyed by S3.

Photo album/title application 1222 can instruct learning model 1230, viacommunication 1256, to set up a ranking interface. As shown in FIG. 12B,communication 1256 includes a Rankinglnterface( ) call with threeparameters: session key S3 and requests for ranked lists of photo titlesand album names, as shown in FIG. 12B, using the predefined PHOTO_TITLEand PHOTO_ALBUM values.

In response, learning model 1230 sends communication 1258 to IIS 1250 torequest identification of image features, such as faces, objects, andlandmarks. As shown in FIG. 12B, learning model 1230 requestsidentifications using the RankingImageProc( ) function with threeparameters: requests to identify faces, objects, and landmarks using therespective pre-defined values of FACES, OBJS, and LANDMARKS. Inresponse, IIS 850 sends an image-identification session key of IP1 viacommunication 1260.

Scenario 1200 continues with learning model 1230 sending communication1262 to built-in features 1250 to request retrieval one or more imagesstored on a mobile platform, as shown in FIG. 12B by passing thepredefined STORED_IMAGES parameter in a ReqBI( ) function call tobuilt-in features 1240. In response, built-in features 1240 returns abuilt-in session key BI3 via communication 1264 to learning model 1230.If authorized to provide stored images by the user/owner of the mobileplatform, built-in features 1244 can provide an image stored on themobile platform to learning model 1230 via communication 1266.Communication 1266 includes a Push( ) function call with threeparameters: built-in session key BI3, identification of a stored imageusing the predefined value STORED_IMAGES, and a reference I1 to thestored image.

Upon receiving the stored image, learning model 1230 provides the storedimage to IIS 1250 via communication 1268 to determine image-relatedfeatures related to the stored image. Communication 1268 includes aPush( ) function call with three parameters: image-identificationsession key IP1, identification of a stored image using the predefinedvalue STORED_IMAGES, and a reference I1 to the stored image.

Scenario 1200 continues with IIS 1250 determining image-related data forthe image referenced by I1 and returning the image-related data tolearning model 1230 via communication 1270. Communication 1270 includesa Push( ) function call with five parameters: image-identificationsession key IP1, a reference I1 to the stored image, image-related datafor faces in F1, image-related data for objects in O1, and image-relateddata for landmarks in L1.

Scenario 1200 continues with built-in features 1240 continuing toprovide images stored on the mobile platform to learning model 1230,which in turn provides each stored image to IIS 1250 to generateimage-related data for faces, objects, and landmarks for each storedimage.

Learning model 1230 then requests platform-related data from built-infeatures 1240 via communication 1272. Communication 1272 includes aReqBI( ) function call with five parameters: built-in session key BI3, apredefined value of SOCIAL_MEDIA requesting all authorized socialnetworking messages stored on the mobile platform, a predefined value ofNAMES requesting all authorized contact names stored on the mobileplatform, a predefined value of EVENTS requesting all authorizedcalendar entries and/or events stored on the mobile platform, andIMAGE_COMMENTS requesting all authorized comments on the STORED_IMAGESpreviously requested. In other scenarios, more or less information canbe requested by learning model 1230 as platform-related data.

As shown in FIG. 12B, in response to communication 1272, built-infeatures 1240 deliver a name referred to as Name1 via communication 1274to learning model 1230. Built-in features 1240 can use additional Push() function calls to provide additional requested and authorizedfeature-related data to learning model 1230.

Scenario 1230 continues with photo album/title application 1222providing an image Image1 to learning model via communication 1276.Photo album/title application 1222 then requests a list of threesuggested photo album names related to Image1 from learning model 1230via communication 1278.

In response to communication 1278, learning model 1230 generates a listof three suggested photo album names related to Image1 and provides therequested list as the “Album[3]” parameter of the Pull response shown incommunication 1280.

FIG. 12B shows that photo album/title application 1222 then requests alist of five suggested photo titles related to Image1 from learningmodel 1230 via communication 1282. In response to communication 1282,learning model 1230 generates a list of five suggested photo titlesrelated to Image1 and provides the requested list as the “Title[5]”parameter of the Pull response shown as part of communication 1284. Inthe present example, scenario 1200 then concludes.

Example Communication Signal Classification Service

Some mobile platforms can search to connect with available communicationnetworks. In Wi-Fi systems, the mobile platform can turn on atransmitter to send one or more probe request messages requestingconnection information, and/or turn on a receiver to listen for a beaconsignal. If a network is available, the mobile platform either receives aprobe response signal to a sent probe message, or receives alistened-for beacon signal. The mobile platform can establish a Wi-Ficonnection with the network after performing authenticated andassociated procedures. If no network is available, the mobile stationdoes not receive a signal; e.g., either a probe request signal or beaconsignal, and so can determine that no network is available.

Thus, to search for a network, the mobile platform has to turn on aradio receiver to listen for signals and, if sending probe messages,perhaps also turn on a radio transmitter to send probe messages. In someembodiments, mobile platforms can continue searching until an availablenetwork is found and a Wi-Fi connection is established. This searchingprocedure can take a relatively large amount of power, thereby reducingpower available for other mobile platform tasks, such as voice and/ordata communication.

To save power, the mobile platform can train and use a machine-learningservice to predict whether or not a search to connect with availablecommunication networks will succeed. The machine-learning service can betrained using a number of features, including, but not limited to:mobile platform location, mobile platform velocity, time, signalcharacteristics such as signal strength, device usage, time since a lastsearch for wireless communication service was performed, and outcome ofthe last search for wireless communication service. By predicting searchoutcomes rather than performing actual searches, the mobile platform cansave power and reduce the amount of signaling used in attempting toconnect with communication networks.

As part of training the machine-learning service, the mobile platformcan carry out a number of searches to connect with availablecommunication networks. At least while training, the mobile platform canstore the above-mentioned features, along with an outcome of each searchto connect, e.g., success or failure. The outcome of each search can betreated as a fact or “ground truth.” In one embodiment, themachine-learning service can include a binary classifier that predicts alikelihood of a finding an available communication network, a.k.a.predicts a result of a search to connect with available communicationnetworks.

In another embodiment, the machine-learning service can include atri-nary classifier that predicts a likelihood of a finding an availablecommunication network or indicates that the service cannot predict theoutcome of a search. For example, if the machine-learning service weretrained to look for communications networks in north-eastern Illinoisonly and the mobile platform was moved to central California, themachine-learning service can output that it cannot predict the outcomeof a search to connect with available communication networks, as thecurrent set of features, especially location, do not match any featuresused to train the machine-learning service.

In still another embodiment, the machine-learning service can getfeatures and communication search outcomes from other mobile platforms,either via direct communication with other mobile platforms or viacommunication with one or more intermediate computing devices, such asservers storing feature and outcome data. In the above example of amobile platform going from north-eastern Illinois to central California,the mobile platform can download feature and outcome data from mobileplatforms in central California, and use the California-based data topredict search outcomes. In still other embodiments, themachine-learning service can learn about and generate predictionsregarding connections with communication networks along with or insteadof Wi-Fi networks, such as, but not limited to, Wi-Max, GSM, CDMA, TDMA,3G, 4G, Bluetooth, and Zigbee networks.

Turning to the figures, FIG. 13 is a flow chart of a method 1300, inaccordance with an example embodiment. In some embodiments, part or allof method 1300 can be executed using one or more mobile platforms; e.g.,mobile platform 200, 502, 602, 802, 1202, 1502, 1852, 2002 and/or one ormore computing devices; e.g., computing device 2200.

Method 1300 begins at block 1310, where a machine-learning serviceexecuting on a mobile platform can receive feature-related data. Thefeature-related data can include communications-related data related toone or more searches for establishing electronic communications receivedfrom an application executing on the mobile platform andplatform-related data received from the mobile platform. Thecommunications-related data and the platform-related data can differ.

At block 1320, the machine-learning service can determine whether or notthe machine-learning service is trained to perform machine-learningoperations related to predicting outcomes of searches for establishingelectronic communications.

In some embodiments, the searches for establishing electroniccommunications can include a search to establish a wireless local-areanetwork connection. In particular embodiments, the search to establishthe wireless local-area network connection can include a search toestablish the wireless local-area network connection based on anInstitute of Electrical and Electronics Engineers (IEEE) 802.11standard.

In other embodiments, in response to determining that themachine-learning service is not trained: additional feature-related datacan be received at the machine-learning service, where the additionalfeature-related data can include additional communications-related datarelated to one or more searches for establishing electroniccommunications received from the application and additionalplatform-related data from the mobile platform, and where thecommunications-related data, additional communications-related data,platform-related data, and additional platform-related data all differ.After receiving the additional feature-related data, determining whetherthe machine-learning service is now trained to perform machine-learningoperations related to predicting outcomes of searches for establishingelectronic communications.

At block 1330, in response to determining that the machine-learningservice is trained: (i) the machine-learning service can receive arequest for a predicted outcome of a search for establishing anelectronic communication, (ii) the machine-learning service can generatethe predicted outcome by performing a machine-learning operation on thefeature-related data, and (iii) the predicted outcome can be sent to theapplication, perhaps by the machine-learning service.

In particular embodiments, the machine-learning service can generate thepredicted outcome by performing a machine-learning operation on thefeature-related data without receiving the request for the predictedoutcome of the search; i.e., the machine-learning service can generatethe predicted outcome of the search without being prompted by therequest.

In some embodiments, method 1300 can further include: in response todetermining that the machine-learning service is trained, acommunication indicating that the machine-learning service is trainedcan be sent to the application, perhaps by the machine-learning service.

In other embodiments, method 1300 can further include: in response tothe predicted outcome indicating a search for establishing electroniccommunications would be successful, a wireless interface of the mobileplatform can be activated. In still other embodiments, in response tothe predicted outcome indicating a search for establishing electroniccommunications would not be successful, deferring activation of a radioof the mobile platform. In particular of the still other embodiments,after receiving the predicted outcome indicating the search forestablishing electronic communications would be not successful, theapplication can start a periodic request for the predicted outcome ofthe search for establishing the electronic communication.

In more particular of the still other embodiments, the applicationstarting the periodic request for the predicted outcome of the searchfor establishing an electronic communication can include: sending therequest for the predicted outcome of the search for establishing theelectronic communication to the machine-learning service, receiving thepredicted outcome from the machine-learning service, in response to thepredicted outcome indicating a search for establishing electroniccommunications would not be successful, waiting at least apre-determined amount of time, and in response to the predicted outcomeindicating a search for establishing electronic communications would besuccessful: stopping the periodic request for the predicted outcome andattempting establishment of the electronic communications.

In even other embodiments, method 1300 can further include: in responseto the predicted outcome indicating failure to predict a search forestablishing electronic communications: conducting the search toestablish electronic communications, determining an outcome of thesearch, and sending an indication of the outcome of the search to themachine-learning service.

FIG. 14 shows a scenario 1400 for establishing wireless communicationswith a number of access points, in accordance with an exampleembodiment. During scenario 1400, a mobile platform travels to a numberof locations and attempts to establish wireless electroniccommunications at each of the locations. Some of the locations areserved by one or more wireless networks, and so some of the attempts toestablish wireless electronic communications are successful while otherattempts are unsuccessful. Records of the attempts to establish wirelesselectronic communications scenario during scenario 1400 are stored inlog 1460, which can be stored on the mobile platform.

The top portion of FIG. 14 depicts a twelve-block map. The fournorth/south streets shown in FIG. 14 are numbered from 1^(st) St., onthe left or west side, 2^(nd) St., just to the east of 1^(st) St.,3^(rd) St. to east of 2^(nd) St. and 4^(th) St., which is the right-mostor east-most north/south street. The three east/west avenues shown inFIG. 14, going from north to south are: Apple Ave, Berry Ave., andCherry Ave.

Several of the blocks shown in FIG. 14 have wireless communicationstransmitters, specifically shown as transmitters 1420, 1422, 1424, 1426,1428, 1430. Transmitter 1420 generates network “4thApple” with a rangeshown using circle 1440. Similarly, transmitters 1422, 1424, 1426, 1428,1430 respectively generate networks “ApBer”, “FanSar Books”, “Zone3”,“C2”, and “BearClaw”, with respective ranges shown using circles 1442,1444, 1446, 1448, 1450.

During scenario 1400, the mobile platform travels from location A, shownin FIG. 14 as an octagon surrounding the letter “A” that is just northof Apple Ave near 2^(nd) St, through locations B, C, D, E, F, and G. Ateach of locations A, B, C, D, E, F, and G, the mobile platform attemptsto connect wirelessly with a wireless network, such as wireless networkconducted using an IEEE 802.11 standard (a.k.a. a Wi-Fi network) orperhaps using another communications protocol. After each attempt toconnect wirelessly, the mobile platform stores data related to theattempt in a log, shown in FIG. 14 as log 1460.

As shown at the bottom of FIG. 14, log 1460 stores the followinginformation:

-   -   a location (Loc) of the mobile platform,    -   a velocity of the mobile platform,    -   a time that the mobile platform attempted to connect with a        wireless network,    -   a signal strength (SigStr) of a wireless network detected at the        time that the mobile platform attempted to connect. In the        example log shown in FIG. 14, signal strengths are rated on a 0        (no signal) to 100 (maximum strength) scale. Other techniques        for rating signal strengths are possible as well.    -   a wireless network name (Ntwk), if detected during the attempt        to connect or 0, if no network detected,    -   a time (Time(S)) since the last “search” or attempt to connect,        and    -   an outcome (Out(S)) of the last attempt to connect.

In other embodiments, more or fewer data items can be collected andstored in log 1460.

In scenario 1400, log 1460 shows that the mobile platform was atlocation A at time 17:00 on Tuesday and attempted to connect to awireless network. The outcome of the attempt was unsuccessful. At 17:00on Tuesday, the mobile platform had been used for two minutes, and ithad been five minutes since the last attempt to connect to a wirelessnetwork.

As another example, log 1460 shows that the mobile platform was atlocation B (just south of the intersection 2^(nd) St. and Apple Ave.) attime 17:10 on Tuesday and attempted to connect to a wireless network.The outcome of the attempt was successful. In particular, the mobileplatform connected to the ApBer network with a signal strength of 40 outof 100. At 17:10 on Tuesday, the mobile platform had been used fortwelve minutes, and it had been ten minutes since the last attempt toconnect to a wireless network.

Information for attempts made by the mobile platform at locations C, D,E, F, and G is also shown in FIG. 14. At locations D (the intersectionof 2^(nd) St. and Cherry Ave.), E (just northwest of the intersection of3^(rd) St. and Cherry Ave.), and G (on the north side of Apple Ave. justeast of 2^(nd) St.), the mobile platform was able to connect torespective networks C2, Zone3, and ApBer with respective signalstrengths of 25, 80, and 12. However, at locations C (midway between1^(st) and 2^(nd) Streets and midway between Berry and Cherry Ayes.) andF (on the northeastern corner of 3^(rd) St. and Berry Ave.), the mobileplatform was unable to connect—at location C, the mobile platform wasable to detect the C2 network, but was unable to connect, perhaps due tothe low signal strength of 3 out of 100. At location F, no network wasdetected.

FIG. 15A shows example user interface 1510 used in scenario 1500, inaccordance with an example embodiment. Scenario 1500 includes userinterface 1510 executing on mobile platform 1502. As shown in FIG. 15A,user interface 1510 includes Wi-Fi application 1520 with Wi-Fi PredictorSetting 1522. FIG. 15A shows that Wi-Fi Predictor Setting 1522 is set toactive and that a Wi-Fi predictor is “Trained to Predict Wi-FiConnections.” The Wi-Fi predictor has a Wi-Fi Predictor Dialog 1524 thatincludes three predictions 1526 a, 1526 b, 1526 c, an Refresh button tore-execute the Wi-Fi predictor and display new predictions beforeexiting dialog 1522, and an close button to close dialog 1524.

Each of predictions 1526 a, 1526 b, 1526 c shown in FIG. 15A indicates aprediction for connecting to a wireless network. For examples,prediction 1526 a indicates that mobile platform 1502 is likely toconnect to the “ApBer” Network, prediction 1526 b indicates that theWi-Fi Predictor indicates that mobile platform 1502 is unlikely toconnect to the “C2”Network, and prediction 1526 c indicates that mobileplatform 1502 is unlikely to connect to the “FanSarBooks” Network,

Each of predictions 1526 a, 1526 b, 1526 c shown in FIG. 15Aadditionally enables a user of Wi-Fi Predictor Dialog 1524 to attempt toconnect with the network mentioned in the prediction. For example, byclicking on the check-mark box underneath the word “Connect?” on theright side of prediction 1526 a, mobile platform 1502 can attempt toconnect to the “ApBer” network.

FIG. 15B shows example communications for scenario 1500, in accordancewith an example embodiment. Scenario 1500 is related to Scenario 1400,as scenario 1500 starts with the same attempts to communicate wirelesslyas shown in log 1460. During scenario 1500, the logged data is providedas training data to a classifier learning model 1530. After trainingwith the logged data, learning model 1530 is then requested to predictcommunications at three additional locations H, I, and J, which areshown in the map depicted in FIG. 14, using the location's lettersurrounded by an octagon. Location H is just southwest of location A,location I is near the northernmost point of 4^(th) St. depicted in FIG.14, and location J is just south of location B.

Wi-Fi application 1520 requests service from learning model 1530 usingthe getService( ) function in communication 1552. In response, learningmodel 1530 sends a learning model session key of “S10” to Wi-Fiapplication 1520 using communication 1554. Session key S10 is includedfor subsequent communications between Wi-Fi application 1520 andlearning model 1530 to permit addressing the subsequent communicationsto the correct learning model, e.g., learning model 1530 and correctapplication, e.g., Wi-Fi application 1520 for a learning session keyedby S10.

Wi-Fi application 1520 can instruct learning model 1530 viacommunication 1520 to set up a classification interface. As shown inFIG. 15B, communication 1556 includes a ClassificationInterface( ) callwith five parameters: session key S10, an input-source reference ofConns, and a requested outputs of Wi-Fi signal strengths, Wi-Fi Networknames, and Wi-Fi search outcomes, as shown in FIG. 15B, using thepredefined WIFI_SIG_STR, WIFI_NTWK, and WIFI_OUTCOME values.

In embodiments not shown in FIG. 15B, an application can request asimilar prediction for a non-Wi-Fi network, such as, but not limited to,a Wi-Max, GSM, CDMA, TDMA, 3G, 4G, Bluetooth, or Zigbee network.

In response, learning model 1530 then requests platform-related datafrom built-in features 1540 via communication 1578. Communication 1578includes a ReqBI( ) function call with five parameters: a predefinedvalue of LOC requesting a current location of the mobile platform, apredefined value of VEL requesting a current velocity of the mobileplatform, a predefined value of CLOCK_TIME requesting a current clocktime, a predefined value of USAGE requesting a current usage value, suchas uptime, of the mobile platform a, and a predefined value ofLAST_WIFI_SEARCH_TIME requesting a time of a last search for a Wi-Finetwork. In other scenarios, more or less information can be requestedby learning model 930 as platform-related data. In response, built infeatures 1540 returns a built-in session key BI10 via communication1560.

Scenario 1500 continues with the mobile platform moving to location Aand searching for Wi-Fi service. As shown in FIG. 14, at location A, themobile platform is unable to connect to a Wi-Fi network. FIG. 15B showsWi-Fi Application 1520 providing information about searching for Wi-Finetworks at location A via communication 1562, which includes a Push( )function call with five parameters. The five parameters are a sessionkey S10, an input-source reference of Conns, a Wi-Fi signal strengthvalue of 0, a Wi-Fi Network name of 0 to indicate no network was found,and a Wi-Fi search outcome of NO to indicate the search wasunsuccessful.

In response to the Push( ) function call of communication 1562, learningmodel 1530 can send communication 1564 with a Push( ) function tobuilt-in features 1540 to request platform-related data. The Push( )function sent via communication 1564 has six parameters: a built-insession key of BI10, a location parameter L1, a velocity parameter V1, aclock-time parameter CT1, a usage parameter U1, and a last Wi-Fi searchtime parameter LWST1. In response, built-in features 1540 can send aPush( ) function via communication 1566 with values for the parametersrequested using communication 1564. As shown in FIG. 15B, communication1566 includes a Push( ) function with a built-in session key of BI10, alocation value of “A” indicating location A as shown in FIG. 14, avelocity value of 2 MPH, a clock-time value of 1700, a usage value of 2minutes, and a last Wi-Fi search time value of 5 minutes.

In response to communication 1566, learning model 1530 and/or built-infeatures 1540 can log part or all of the information provided by Wi-FiApplication 1520 and/or built-in features 1540, such as shown in log1460 of FIG. 14.

Scenario 1500 continues with the mobile platform moving to location Band searching for Wi-Fi service. As shown in FIG. 14, at location B, themobile platform is able to connect to the “ApBer” network. FIG. 15Bshows Wi-Fi Application 1520 providing information about searching forWi-Fi networks at location B via communication 1568, which includes aPush( ) function call with five parameters. The five parameters are asession key S10, an input-source reference of Conns, a Wi-Fi signalstrength value of 40, a Wi-Fi Network name of ApBer to indicate that theApBer network was found, and a Wi-Fi search outcome of YES to indicatethe search was successful.

In response to the Push( ) function call of communication 1568, learningmodel 1530 can send communication 1570 with a Push( ) function tobuilt-in features 1540 to request platform-related data. The Push( )function sent via communication 1570 has six parameters: a built-insession key of BI10, a location parameter L1, a velocity parameter V1, aclock-time parameter CT1, a usage parameter U1, and a last Wi-Fi searchtime parameter LWST1. In response, built-in features 1540 can send aPush( ) function via communication 1572 with values for the parametersrequested using communication 1570. As shown in FIG. 15B, communication1572 include a Push( ) function with a built-in session key of BI10, alocation value of “B” indicating location B as shown in FIG. 14, avelocity value of 3 MPH, a clock-time value of 1710, a usage value of 12minutes, and a last Wi-Fi search time value of 10 minutes.

In response to communication 1572, learning model 1530 and/or built-infeatures 1540 can log part or all of the information provided by Wi-FiApplication 1520 and/or built-in features 1540, such as shown in log1460 of FIG. 14.

Scenario 1500 continues with the mobile platform traveling to locationsC, D, E, and F as shown in FIG. 14. At each location, the mobileplatform searches to find one or more Wi-Fi networks. The resultingcommunication search data and platform-related data are shown in log1460 of FIG. 14, but are not shown in FIG. 15B to save space.

Scenario 1500 then continues with the mobile platform traveling tolocation G and searching for Wi-Fi service. As shown in FIG. 14, atlocation G, the mobile platform is able to connect to the “ApBer”network. FIG. 15B shows Wi-Fi Application 1520 providing informationabout searching for Wi-Fi networks at location G via communication 1574,which includes a Push( ) function call with five parameters. The fiveparameters are a session key S10, an input-source reference of Conns, aWi-Fi signal strength value of 12, a Wi-Fi Network name of ApBer toindicate that the ApBer network was found, and a Wi-Fi search outcome ofYES to indicate the search was successful.

In response to the Push( ) function call of communication 1574, learningmodel 1530 can send communication 1576 with a Push( ) function tobuilt-in features 1540 to request platform-related data. The Push( )function sent via communication 1576 has six parameters: a built-insession key of BI10, a location parameter L1, a velocity parameter V1, aclock-time parameter CT1, a usage parameter U1, and a last Wi-Fi searchtime parameter LWST1. In response, built-in features 1540 can send aPush( ) function via communication 1578 with values for the parametersrequested using communication 1576. As shown in FIG. 15B, communication1578 include a Push( ) function with a built-in session key of BI10, alocation value of “G” indicating location G as shown in FIG. 14, avelocity value of 2 MPH, a clock-time value of 2002, a usage value of122 minutes, and a last Wi-Fi search time value of 17 minutes.

In response to communication 1576, learning model 1530 and/or built-infeatures 1540 can log part or all of the information provided by Wi-FiApplication 1520 and/or built-in features 1540, such as shown in log1460 of FIG. 14.

In scenario 1500, learning model 1530 is trained after receiving thedata from communications 1576 and 1578. FIG. 15B shows that learningmodel 1530 indicates it is trained by sending communication 1580 toWi-Fi Application 1520 with a Notify( ) function call including twoparameters: a session key S10, and a predefined value of LM_TRAINED toindicate that learning model 1530 is now trained.

Scenario 1500 continues with the mobile platform traveling to location Hand requesting a prediction of a Wi-Fi search outcome. To request theprediction of the Wi-Fi search outcome, Wi-Fi Application 1520 can sendcommunication 1582 with a Pull( ) function call. As shown in FIG. 15B,the Pull( ) function call has two parameters: a session key S10, and apre-defined value WIFI_SEARCH, indicating that Wi-Fi Application 1520 isrequesting a prediction of an outcome of search for Wi-Fi networks fromlearning model 1530.

In some embodiments not shown in FIG. 15B, the Pull( ) function call assent in communication 1582 can have more than two parameters. Forexample, a third parameter specifying a network name, e.g., Zone3, canbe interpreted by learning model 1530 as a request for a prediction fora search of wireless communications with the specified network. Asanother example, a third parameter specifying a location, e.g., locationD, can be interpreted by learning model 1530 as a request for aprediction for a search of wireless communications at the specifiedlocation. Combining these examples, parameters specifying both a networkname and a location can be interpreted by learning model 1530 as arequest for a prediction for a search of wireless communications at thespecified location for the specified network. Many other possiblerequests for prediction of searches for wireless communication arepossible as well.

In response to communication 1582, learning model 1530 can sendcommunication 1584 a with a Push( ) function to built-in features 1540to request platform-related data. The Push( ) function sent viacommunication 1584 a has six parameters: a built-in session key of BI10,a location parameter L1, a velocity parameter V1, a clock-time parameterCT1, a usage parameter U1, and a last Wi-Fi search time parameter LWST1.In response, built-in features 1540 can send a Push( ) function viacommunication 1584 b with values for the parameters requested usingcommunication 1584 a. As shown in FIG. 15B, communication 1584 bincludes a Push( ) function with a built-in session key of BI10, alocation value of “H” indicating location H as shown in FIG. 14, avelocity value of 0 MPH, a clock-time value of 2005, a usage value of125 minutes, and a last Wi-Fi search time value of 3 minutes.

Learning model 1530 can then classify the data received from built-infeatures 1540 into one of two possible predicted outcomes: a predictionthat a search for a Wi-Fi network will be successful, and a predictionthat a search for a Wi-Fi network will be unsuccessful. In someembodiments, a third possible outcome that the learning model is unableto predict the outcome, perhaps because the learning model is nottrained to generate a prediction given the current data, is possible aswell. In still other embodiments, other predictions are possible aswell.

In scenario 1500, learning model 1530 can predict that a search for aWi-Fi connection at location H is unlikely to succeed, perhaps based onrecent data from nearby-location A. Learning model 1530 can sendcommunication 1586 a to inform Wi-Fi Application 1520 of the prediction.FIG. 15B shows that communication 1586 a includes a PullResp( ) functioncall with five parameters: a session key S10, a input-source referenceConns, a predicted Wi-Fi signal strength of 0, a predicted Wi-Fi networkof none indicated using the predefined value of NO_NTWK, and a predictedWi-Fi search outcome of no network found indicated using the predefinedvalue of PRED_NO.

In response to the prediction of a search for a Wi-Fi connection, Wi-FiApplication 1520 can determine that the wireless interface (WI)activation to search for and/or connect to a Wi-Fi network can bedeferred, as shown in box 1586 b of FIG. 15B, as learning model 1530 haspredicted the search and/or attempt to connect would be unsuccessful.Wi-Fi Application 1520 can save power for the mobile platform bydeferring wireless interface activation and making fewer unsuccessfulsearches for Wi-Fi connections.

In some embodiments, in response to the unsuccessful prediction, Wi-FiApplication 1520 can make one or more additional requests forpredictions of searches for Wi-Fi connections. For example, Wi-FiApplication 1520 can periodically request predictions of searches forWi-Fi connections, such as requesting one prediction every N seconds,where N>0. In other embodiments, Wi-Fi Application 1520 can requestanother prediction of a search for Wi-Fi connections when the mobileplatform has moved more than a threshold amount from a location of anunsuccessful search. For example, if the threshold amount is 100 yards,then Wi-Fi Application 1520 can request another predicted search whenmobile platform moves more than 100 yards from location H. Otherresponses to unsuccessful predictions are possible as well.

Scenario 1500 continues with the mobile platform traveling to location Iand requesting a prediction of a Wi-Fi search outcome. To request theprediction of the Wi-Fi search outcome, Wi-Fi Application 1520 can sendcommunication 1588 with a Pull( ) function call. As shown in FIG. 15B,the Pull( ) function call has two parameters: a session key S10, and apre-defined value WIFI_SEARCH, indicating that Wi-Fi Application 1520 isrequesting a prediction of an outcome of search for Wi-Fi networks fromlearning model 1530.

In response to communication 1588, learning model 1530 can sendcommunication 1590 a with a Push( ) function to built-in features 1540to request platform-related data. The Push( ) function sent viacommunication 1590 a has six parameters: a built-in session key of BI10,a location parameter L1, a velocity parameter V1, a clock-time parameterCT1, a usage parameter U1, and a last Wi-Fi search time parameter LWST1.In response, built-in features 1540 can send a Push( ) function viacommunication 1590 b with values for the parameters requested usingcommunication 1590 a. As shown in FIG. 15B, communication 1590 bincludes a Push( ) function with a built-in session key of BI10, alocation value of “I” indicating location I as shown in FIG. 14, avelocity value of 3 MPH, a clock-time value of 2015, a usage value of135 minutes, and a last Wi-Fi search time value of 13 minutes.

In scenario 1500, learning model 1530 can determine that learning model1530 is unable to predict an outcome of a search for a Wi-Fi connectionat location I. For example, learning model 1530 can determine thattraining data does not apply to locations, velocities, clock times,usage values, and/or last search times as provided in communication 1590b, and so determine that learning model 1530 is unable to predict anoutcome of a search for a Wi-Fi connection at location I moving at 3 MPHat 20:15 with 135 minutes of usage and with 13 minutes since a lastWi-Fi search for a Wi-Fi connection.

Learning model 1530 can send communication 1592 to inform Wi-FiApplication 1520 of the inability of learning model 1530 to make aprediction. FIG. 15B shows that communication 1592 includes a PullResp() function call with five parameters: a session key S10, a input-sourcereference Conns, a predicted Wi-Fi signal strength of 0, a predictedWi-Fi network of none indicated using the predefined value of NO_NTWK,and a predicted Wi-Fi search outcome of an unknown outcome as indicatedusing the predefined value of PRED_UNK. Upon observing the predefinedvalue of PRED_UNK, Wi-Fi Application 1520 can then determine thatlearning model 1530 to unable make a prediction.

In response to the unknown prediction of a search for a Wi-Ficonnection, Wi-Fi Application 1520 can determine that activating thewireless interface to search for and/or connect to a Wi-Fi network canbe deferred, as learning model 1530 has not predicted the search and/orattempt to connect would be successful.

In some embodiments not shown in FIG. 15B, in response to the unknownprediction, Wi-Fi Application 1520 can activate the wireless interface,search for Wi-Fi connections, and provide search result data to learningmodel 1530, such as performed via communications 1562, 1568, and 1574.Other responses to prediction unavailability are possible as well.

Scenario 1500 continues with the mobile platform traveling to location Jand requesting a prediction of a Wi-Fi search outcome. To request theprediction of the Wi-Fi search outcome, Wi-Fi Application 1520 can sendcommunication 1594 with a Pull( ) function call. As shown in FIG. 15B,the Pull( ) function call has two parameters: a session key S10, and apre-defined value WIFI_SEARCH, indicating that Wi-Fi Application 1520 isrequesting a prediction of an outcome of search for Wi-Fi networks fromlearning model 1530.

In response to communication 1594, learning model 1530 can sendcommunication 1596 a with a Push( ) function to built-in features 1540to request platform-related data. The Push( ) function sent viacommunication 1596 a has six parameters: a built-in session key of BI10,a location parameter L1, a velocity parameter V1, a clock-time parameterCT1, a usage parameter U1, and a last Wi-Fi search time parameter LWST1.In response, built-in features 1540 can send a Push( ) function viacommunication 1596 b with values for the parameters requested usingcommunication 1596 a. As shown in FIG. 15B, communication 1596 bincludes a Push( ) function with a built-in session key of BI10, alocation value of “J” indicating location J as shown in FIG. 14, avelocity value of 18 MPH, a clock-time value of 2022, a usage value of142 minutes, and a last Wi-Fi search time value of 20 minutes.

In scenario 1500, learning model 1530 can predict that a search for aWi-Fi connection at location J is likely to succeed, perhaps based onrecent data from nearby-location B. Learning model 1530 can sendcommunication 1598 a to inform Wi-Fi Application 1520 of the prediction.FIG. 15B shows that communication 1598 a includes a PullResp( ) functioncall with five parameters: a session key S10, a input-source referenceConns, a predicted Wi-Fi signal strength of 36, a predicted Wi-Finetwork name of “ApBer”, and a predicted Wi-Fi search outcome a networkfound indicated using the predefined value of PRED YES.

In response to the prediction of a successful search for a Wi-Ficonnection, Wi-Fi Application 1520 of mobile platform 1502 can activatethe wireless interface, as shown in box 1598 of FIG. 15B. Once thewireless interface is activated, mobile platform 1502 can search forand/or connect to a Wi-Fi network, as learning model 1530 has predictedthe search and/or attempt to connect would be successful. Otherresponses to successful predictions are possible as well.

Example Session Duration Prediction Service

A “usage session” for a mobile platform is the span of time between whenthe mobile platform is awakened or perhaps powered up and when themobile platform is put to sleep or perhaps powered down. The time spanor duration of the usage session is the amount of time between theawakening of the mobile platform and putting the mobile platform tosleep. For example, if a mobile platform is powered up at 8:00 AM andpowered down at 8:05 AM, the time span of the usage session starting at8:00 AM is five minutes.

The machine-learning service can predict a time span of a usage sessioneither numerically or via classification. An example numericalprediction would be that a usage session will be six minutes long. Anexample classification of the duration of the usage session can be:short (e.g., less than five minutes), medium (e.g., between five and tenminutes), or long (greater than five minutes). In an example utilizingthese classifications, the machine-learning service can predict a usagesession will be either short, medium, or long.

A software application can use a predicted session duration or time spanto guide the application's behavior. For example, suppose a user powersup a mobile platform and then starts a jukebox application to playrandom audio and/or video files. The jukebox application can send arequest to the machine-learning service to predict a time span for thecurrent usage session. In this example, the machine-learning servicepredicts a “medium” (5-10 minute) time span for the usage session. Basedon the predicted medium time span, the jukebox application can initiallyselect only audio, video, and/or audio-video files that cumulativelytake ten minutes or less to play based on the predicted medium timespan.

Further, suppose the jukebox application first plays an audio, video,and/or audio-video file that takes three minutes. Then, in selecting asecond audio, video, and/or audio-video file, the jukebox applicationcan select files that take no more than seven minutes long, based on aten-minute long maximum duration of the usage session, with threeminutes already consumed by playing the first file. Many other examplesof application requests and using predicted session durations and/ortime spans are possible as well.

Turning to the figures, FIG. 16 is a flow chart of a method 1600, inaccordance with an example embodiment. In some embodiments, part or allof method 1600 can be executed using one or more mobile platforms; e.g.,mobile platform 200, 502, 602, 802, 1202, 1502, 1852, 2002 and/or one ormore computing devices; e.g., computing device 2200.

At block 1610, feature-related data can be received at amachine-learning service executing on a mobile platform. Thefeature-related data can include usage-related data and platform-relateddata. The usage-related data can include data about one or more timespans that the mobile platform is activated. The platform-related datacan be received from the mobile platform. The usage-related data and theplatform-related data can differ.

At block 1620, the machine-learning service can determine whether themachine-learning service is trained to perform machine-learningoperations related to predicting a time span that the mobile platformwill be activated.

In some embodiments, the machine-learning operations can include aclassification operation.

In other embodiments, the machine-learning operations can include aregression operation. In particular embodiments, the regressionoperation can include a linear regression operation. In other particularembodiments, the predicted time span can include a predicted amount forthe predicted time span.

At block 1630, in response to determining that the machine-learningservice is trained, the machine-learning service can: receive a requestfor a predicted time span that the mobile platform will be activated,determine the predicted time span by performing a machine-learningoperation on the feature-related data, and send the predicted time span.

In particular embodiments, the machine-learning service can determinethe predicted time span by performing a machine-learning operation onthe feature-related data without receiving the request for the predictedtime span; i.e., the machine-learning service can generate the predictedtime span without being prompted by the request.

In some embodiments, the predicted time span can include a predictedclassification of the time span. In particular embodiments, thepredicted classification of the time span can be selected from among ashort time span, a medium time span, and a long time span. In otherembodiments, method 1600 can include selecting one or more media filesbased on the predicted time span and presenting the selected one or moremedia files using the mobile platform, such as discussed above in thecontext of the example jukebox application.

FIG. 17A shows usage log 1700 for activations of a mobile platform, inaccordance with an example embodiment. Usage log 1700 shows, for eachactivation of the mobile platform, a time span (TS), a starting timeshown using military-time notation, a location number, a location label(Loc Label), and a time-span label (TS Label).

Semantic labels, such as the location label and time-span label, can bepart of usage log 1700. For example, the first row of usage log 1700corresponds to an eight-minute long usage session starting at 8:08 AM(0808) at location 1, also known as “Home”, and the time span of“medium” duration. For the entries in usage log 1700, a usage sessionwith a time span of zero to five minutes is labeled “short”, a usagesession with a time span of five to ten minutes is labeled “medium”, anda usage session with a 10+ minute time span is labeled “long.” In someembodiments, more, fewer, and/or other semantic labels for time spanscan be used. In other embodiments, other ranges of times can be used forshort, medium, and/or long time spans.

Additionally, locations can have semantically labels. As shown in FIG.17A, seven separate locations are listed, both numerically using thenumbers one through seven, and with semantic labels. FIG. 17A showslocation 1 with a semantic location label of “Home”, location 2 with asemantic location label of “Work”, location 3 with a semantic locationlabel of “Random1”, location 4 with a semantic location label of“Groceries”, location 5 with a semantic location label of “Lunch Rest.”abbreviating Lunch Restaurant, location 6 with a semantic location labelof “Movie” for a movie theater, and location 7 with a semantic locationlabel of “Random2.”

FIG. 17B shows a usage log 1710 of activations of a mobile platform at aparticular location, in accordance with an example embodiment. Inparticular, usage log 1710 shows activations of the mobile platformlogged in usage log 1700 at location four, which has a semantic locationlabel of “Groceries” as shown in FIG. 17A. The time-span data in usagelog 1710 ranges from a minimum time-span of 15 minutes in the last entryof usage log 1710 to a maximum time-span of 22 minutes in the secondentry of usage log 1710.

Usage log 1710 also includes a “Start” column with starting times forusage sessions shown in military time notation and a “Mins” column withstarting times for usage sessions shown in terms of the number ofminutes since midnight. For example, the first row of usage log 1710 inthe “Start” column is for a usage session of 17 minutes duration thatstarted at 17:59, where the time 17:59=17*60+59=1,079 minutes aftermidnight.

FIG. 17C shows graph 1720 of a linear regression model based on the datain usage log 1710, in accordance with an example embodiment. To providea numerical prediction for a time span, a learning model can useregression techniques, such as, but not limited to linear regression,multiple regression, multivariate linear regression, and non-linearregression techniques.

The linear regression model shown in FIG. 17C is based on the “TS” and“Mins” column data. Mins column data were used instead of “Start” columndata, as the Mins column data covers a single range of integers from 0(corresponding to 0000 military time of 0000) to 1439 (corresponding to2359 military time), while the set of military time values involve 24separate ranges of integers (0000-0059, 0100-0159, . . . 2300-2359).

Specifically, the linear regression model shown is based on the equationy=mx+b, where m is approximately −0.04 and b is approximately 61. WhileMins column data were used to determine the linear regression model, thex or “Start” axis of graph 1720 is labeled using military time labelsfor convenience. For example, the x axis label “0600” shown in FIG. 17Crepresents the time 6:00 AM.

The linear regression model can predict numerical values for time spans.For example, suppose the machine-learning model was requested to predicta time span of a usage session for the mobile platform at location“Groceries” at 6 PM or 1800 military time. The machine-learning servicecan use the linear regression model shown in FIG. 17C to predict a timespan of 18.85 minutes for the usage session starting at 1800.

FIG. 18A shows classification diagram 1800, which based on the data inusage log 1700 shown in FIG. 17A, in accordance with an exampleembodiment. Classification diagram 1810 depicts a result of a possibleanalysis of the data in usage log 1700 that can be used by themachine-learning service to predict time spans of usage sessions.

Classification diagram 1800 begins at node 1810, where a location isdetermined. FIG. 18A shows locations using the some of the semanticlabels shown in FIG. 17A, such as Home used in node 1820, Work used innode 1822, Groceries used in node 1824, and Lunch Rest. used in node1826. Node 1828 uses a label of “Single-Session Locs.” to group all ofthe locations involved in one usage session as recorded in usage log1700. Node 1828 include data generated for locations with semanticlabels “Movie”, “Random”, and “Random2” in usage log 1700. In otherembodiments, numeric locations can replace the use of semantic locationlabels in classification diagram 1800.

In the example shown in FIG. 18A, a short time span can range from zeroto five minutes, a medium time span can range from six to ten minutes,and a long time span ranges longer than eleven minutes. Specifically,node 1820 shows an analysis of usage-session time spans from usage log1700 at location Home, indicating that at location Home, six of sixteenof total sessions or 37.5% had a short time span, seven of sixteen totalsessions or 43.8% had a medium time span, and three of sixteen totalsessions or 18.8% had a long time span. In some embodiments, more,fewer, and/or other semantic labels for time spans can be used. In otherembodiments, other ranges of times can be used for short, medium, and/orlong time spans.

Node 1822 shows an analysis of usage-session time spans at locationWork, indicating that at location Work, ten of twelve total sessions or83.3% had a short time span, two of twelve total sessions or 16.7% had amedium time span, and zero sessions had a long time span.

Only one semantic time label is shown as being utilized for each ofnodes 1824, 1826, and 1828. For node 1824, representing locationGroceries, all four usage sessions had long time spans. Node 1826,regarding location Lunch Rest., indicates that both usage sessions atthat location had short time spans. Node 1828, reflecting data from theSingle-Session Locations, indicates that all three usage sessions atthose locations had short time spans.

At the Home and Work locations, multiple possible classifications oftime spans are possible. In that case, the data for those locations canbe additionally analyzed. For example, node 1830, shown connected tonode 1820 in classification diagram 1800, indicates a result of furtheranalysis of usage sessions for location “Home” based on bothusage-session time spans and time of day. In node 1830, times of day areclassified as either before work (BW), during work (DW), or after work(AW). For example, the BW times of day can range from midnight to 8:59AM, the DW times of day can range from 9:00 AM to 5:00 PM, and the AWtimes of day can range from 5:01 PM to 11:59 PM. In some embodiments,more, fewer, and/or other semantic labels for times of day can be used.In other embodiments, other ranges of times can be used for the BW, DW,and AW time spans. In still other embodiments, times of day can be usedwithout classifications.

Node 1830 of FIG. 18A shows that: (a) three of seven usage sessionsbefore work at location Home have short time spans and four usagesessions at location Home have medium time spans, (b) no usage sessionswere made at location Home during work, and that (c) equal numbers ofshort, medium, and long duration usage sessions were made at locationHome after work. Using the information shown in node 1830, if aprediction is requested while the mobile platform is at location Home,one prediction is that the call is slightly more likely to be of mediumduration than short duration before work, and that all durations areequally likely either during work or after work. As there are data forafter-work calls, additional analysis may be performed to better refinethe after work call estimate that “all durations are equally likely”;e.g., use additional time span and/or time-of-day classifications forafter-work calls.

For node 1832, times of day are classified as either “AM” for morningwork hours; e.g., between 9:01 AM and 11:59 AM, “Meal” or during a meal,a.k.a. lunch or dinner; e.g., between 12:00 PM and 12:59 PM, and “PM”for post-meal afternoon work hours; e.g., between 1:00 PM and 4:59 PM.In some embodiments, more, fewer, and/or other semantic labels for timesof day can be used. In other embodiments, other ranges of times can beused for the AM, Meal, and PM times of day. In still other embodiments,times of day can be used without classifications. Node 1832 of FIG. 18Ashows that: (a) all AM-time usage sessions have short time spans, (b)all Meal-time usage sessions have short time spans, and (c) it issomewhat more likely that short time-span usage sessions will be madeduring the PM times of day than medium time-span usage sessions.

Classifications and classification diagram 1800 can be used to generatenumerically-valued predictions. For example, using the data in node1820, if a usage session is begun at location Home, 37.5% of all usagesessions are short, 43.8% of all usage sessions are medium, and 18.8% ofall usage session are long. To generate a numerical prediction, one setof assumptions that can be used is that the mid-point value of a givenrange is chosen to represent usage-sessions in the range, and a longtime-span usage session is arbitrarily chosen to have a 20 minute longtime span.

Under these assumptions and for the data in node 1820, a predicted timespan PT for a usage session at location Home can be determined as:PT=short_percent*short_midpoint+medium_percent*medium_midpoint+long_percent*long_arbitrary_value=37.5%*2.5 minutes+43.8%*8 minutes+18.8%*20 minutes=0.94+3.50+3.76=8.20 minutes.

Other techniques for generating numerically-valued predictions fromclassifications and classification diagrams are possible as well.

FIG. 18B shows example user interface 1854 for jukebox application 1860used in scenario 1850, in accordance with an example embodiment. Inscenario 1850, user interface 1854 shows feature-related data display1856 and jukebox application display 1860. Feature-related data displayshows a current time and date of “12:23 Sat.”, a current location with asemantic label of “Home”, and current weather conditions of “58° F.” and“Cloudy.”

A jukebox application can be configured to request the machine-learningservice to predict a time span of a usage session and then select media,including but not limited to audio, video, and audio/video files, toplay during the predicted duration. In scenario 1850, the jukeboxapplication generates jukebox application display 1860, which includesprediction dialog 1862, media list dialog 1864, play list button 1870,and close button 1872.

Prediction dialog 1862 shows the usage session time span predicted bythe machine-learning service of 10 minutes, perhaps based on thefeature-related data shown in feature-related display 1856, such as thedisplayed location, date, and time. Prediction dialog 1862 includes twobuttons: a “Change” button to permit the user to change the usagesession time span from the predicted value and a “Dismiss” button toclose prediction dialog 1862.

As shown in FIG. 18B, media list dialog 1864 displays a media listselected by the jukebox application. The media list includes three mediafiles to be played: “Two-Minute News” with a duration of 2:03, a“Favorite Song 1” with a duration of 4:32, and “Now & Future Tools” witha duration of 3:11.

In scenario 1850 and as shown in FIG. 18B, the “Now & Future Tools”media is selected, as shown in FIG. 18B using a grey background for the“Now & Future Tools” media list item and the bold font for the “Now &Future Tools” media list entry. As such, selected media and controls1866 operate on the “Now & Future Tools” media. Selected media andcontrols 1866 include a “Play” button to play the selected mediaimmediately, a “Delete” button to delete the selected media from themedia list, a “Move” button to enable movement of the selected mediawithin the media list, and a “New . . . ” button to permit addition ofnew media item(s) to the media list. Other dialogs and controls arepossible as well.

Usage information 1868 shows the total duration of the three media filesis 9 minutes, 46 seconds (9:46) and the current usage, or amount of timemobile platform 1852 has been activated, is 3 seconds (0:03). Inscenario 1850, the media shown in media list dialog 1864 were selected,in part, to play in total less than the predicted session time span. Inscenario 1850 and as shown in FIG. 18B, the total duration of the threemedia files is 14 seconds less than the predicted duration of 10minutes.

Play list button 1870 can be used to play the media items in the ordershown in media list dialog 1864. Close button 1872 can be used to closeuser interface 1854. In some embodiments, media files played using thejukebox application can be played in the background, to permit the userof mobile platform 1852 to interact with other applications during theusage session. In particular embodiments, when the jukebox applicationis in the background and is about to play a video or audio/video file,the jukebox application can attempt to become a foreground applicationto permit the user to view the video or audio/video file.

Example Automated Volume Setting Service

Volume settings for mobile platforms, such as ring tone volume, mute,and vibrate settings, are often changed according to predictablepatterns. For example, before attending meetings, a mobile platform usercan use a mute setting to turn off a telephone ringer. At that time, theuser may set the mobile platform to a vibrate mode, where a user of themobile platform is alerted to an incoming telephone call by use of ahaptic or vibration system rather than the telephone ringer.

Once the meeting is complete, the user may “un-mute” the mobile platformso to turn on the telephone ringer. At that time, the user may also setthe vibrate mode to off, so to turn off the haptic or vibration system,and thus rely on the telephone ringer to alert the user of an incomingtelephone call. The user may perform similar actions at other placesthan meetings where silence is often expected, such as at movietheaters, libraries, schools, lecture halls, live performances, andsimilar situations.

The machine-learning service can be trained to learn patterns inchanging volume settings for a mobile platform. Once trained, themachine-learning service can generate predictions for the volumesettings. The predictions can be used to have the mobile platformdirectly or indirectly change the volume settings. For example, themachine-learning service can provide the predicted settings to a dialerapplication or other application that changes the settings to thepredicted setting values. As another example, the machine-learningservice can generate a prompt with the predicted setting values, perhapswith a reminder to the user to change the volume settings.

By predicting volume settings for mobile platforms, the mobile-learningservice can save the user the effort and time involved in repeatedlysetting and resetting volume settings. Additionally, if the mobileplatform is configured to prompt the user to change volume settings whenpredicted, the user can be reminded to change volume settings beforeattending an event where silence is often expected, and thus avoid theembarrassment of having the mobile platform sound off during the event.

Turning to the figures, FIG. 19 is a flow chart of a method 1900, inaccordance with an example embodiment. In some embodiments, part or allof method 1900 can be executed using one or more mobile platforms; e.g.,mobile platform 200, 502, 602, 802, 1202, 1502, 1852, 2002 and/or one ormore computing devices; e.g., computing device 2200.

At block 1910, feature-related data can be received at amachine-learning service executing on a mobile platform. Thefeature-related data can include volume-related data andplatform-related data. The volume-related data can include data aboutone or more volume-related settings for the mobile platform. Theplatform-related data can be received from the mobile platform. Thevolume-related data and the platform-related data can differ.

In some embodiments, the one or more volume-related settings can includea mute setting, a vibration setting, and a setting for ringer volume. Inother embodiments, the feature-related data can include data related toa location of the mobile platform, a current time, a call-terminationtime, a calling party, and a calendar event. In still other embodiments,the platform-related data can include data about devices proximate tothe mobile platform.

At block 1920, the machine-learning service can determine whether themachine-learning service is trained to perform machine-learningoperations related to predicting a change in the one or morevolume-related settings for the mobile platform.

At block 1930, in response to determining that the machine-learningservice is trained, the machine-learning service can: receive a requestfor predicting the change in the one or more volume-related settings,determine the predicted change in the one or more volume-relatedsettings by performing a machine-learning operation on thefeature-related data, and send the predicted change in the one or morevolume-related settings.

In particular embodiments, the machine-learning service can determinethe predicted change in the one or more volume-related settings byperforming a machine-learning operation on the feature-related datawithout receiving the request for the predicted outcome of the searchfor establishing the electronic communication; i.e., themachine-learning service can generate the predicted outcome withoutbeing prompted by a request.

In some embodiments, method 1900 can further include determining one ormore semantic labels for the platform-related data. In particular of thesome embodiments, determining one or more semantic labels for theplatform-related data can include determining the one or more semanticlabels for the current time, where the semantic labels include anat-home-time label, a work-time label, a sleep-time label, atraveling-time label, and a meal-time label.

In other particular of the some embodiments, determining one or moresemantic labels for the platform-related data includes determining theone or more semantic labels for the current location, where the semanticlabels include a home-location label, a work-location label, asleep-location label, a traveling-location label, and a meal-locationlabel.

In still other embodiments, method 1900 can further include: determiningwhether the predicted change in the one or more volume-related settingscan include a prediction that at least one of the one or morevolume-related settings change. In response to determining that thepredicted change in the one or more volume-related settings includes theprediction that the at least one of the one or more volume-relatedsettings changes, a prompt to change the at least one of the one or morevolume-related settings can be generated.

In even other embodiments, method 1900 can further include: determiningwhether the predicted change in the one or more volume-related settingscan include a prediction that at least one of the one or morevolume-related settings change. In response to determining that thepredicted change in the one or more volume-related settings includes theprediction that the at least one of the one or more volume-relatedsettings changes, the at least one of the one or more volume-relatedsettings can be changed. The volume-related settings can be changed bythe mobile platform without additional input, perhaps by a dialerapplication executing on the mobile platform that receives the at leastone of the prediction that the one or more volume-related settings fromthe machine-learning service and sets the volume-related settingsaccording to the prediction.

FIG. 20A shows an example user interface 2010 used in scenario 2000, inaccordance with an example embodiment. User interface 2010, executing onmobile platform 2002, includes an interface for dialer application 2020configured to generate and display volume dialog 2022. Volume dialog2022 includes manual volume settings 2024, smart volume setting 2026, anOK button to save current settings before exiting volume dialog 2022,and a Cancel button to exit volume dialog 2022 without saving currentsettings.

Manual volume settings 2024 includes a slider bar that enables a user toset a microphone output setting between a minimum setting of 0, whicheffectively mutes a ringer that outputs the ringtone, and a maximumsetting of 100, which provides the maximum output volume for theringtone. In scenario 2000 as shown in FIG. 20A, manual ringtone volumesetting is set at 51.

Manual volume settings 2024 also includes check boxes for a mute settingand a vibrate setting. The mute setting, when active or checked,inhibits ringer output; a.k.a. mutes the ringer and stops ringtoneoutput. The vibrate setting, when active or checked, allows a hapticoutput for the ringer; a.k.a. the mobile platform can vibrate or produceother haptic output when the mobile platform is in a ringing state. Asshown in FIG. 20A, both the mute and vibrate settings are not active orchecked. In scenario 2000, when manual volume settings are allowed, themute setting is not active and so the slider bar sets the volume for theringer. Also, no haptic output is generated when the mobile platform isin the ringing state.

As shown in FIG. 20A, smart volume setting 2026 can learn and predictringer volume, mute, and vibrate settings. When smart volume setting2026 is disabled, dialer application 2020 can use manual volume settings2024 to determine output volume for a ringer of mobile platform 2020 andwhether or not haptic output will be provided when mobile platform 2002is in the ringing state.

When smart volume setting 2026 is enabled, dialer application 2020 canuse a machine-learning service to perform machine-learning operation(s)to predict volume settings, such as ringtone volume, mute, and vibratesettings. Then, upon receiving predicted volume setting value(s), dialerapplication 2020 can use the predicted settings values according topredicted usage setting 2028.

In scenario 2000, and as shown in FIG. 20A, smart volume setting 2026 isset to enabled and predicted usage setting 2028 is set to apply. Whenpredicted usage setting 2028 is set to apply, predicted volume settingvalue(s), such as ringtone volume, mute, and/or vibrate setting values,received by dialer application 2020 are used to set the ringtone volume,mute, and vibrate settings. That is, when predicted usage setting 2028is set to apply, mobile platform 2002 and/or dialer application 2020,can set the ringtone volume, mute, and/or vibrate settings using thepredicted volume setting values(s) without additional input, such asuser input. In some embodiments, when predicted usage setting 2028 isset to apply, mobile platform 2002 and/or dialer application 2020 cangenerate a dialog or other prompt to indicate to a user that theringtone volume, mute, and/or vibrate settings have been changed to thepredicted volume setting values(s).

When predicted usage setting 2028 is set to prompt, predicted volumesetting value(s) received by dialer application 2020 are displayed,audibly output, and/or otherwise prompted to a user of mobile platform2002. Upon being displayed, the user can use volume dialog 2022 to setthe ringtone volume, mute, and vibrate settings as desired, perhapsusing the prompted predicted volume setting value(s).

FIG. 20B shows example communications for the scenario 2000, inaccordance with an example embodiment. In FIG. 20B, scenario 2000continues with dialer application 2020 calling the getService( )function of learning model 2030 via communication 2052. In response,learning model 2030 provides a session key S11 via communication 2054 todialer application 2020. Session key S11 is included for subsequentcommunications between dialer application 2020 and learning model 2030to permit addressing the subsequent communications to the correctlearning model, e.g., learning model 2030 and correct application, e.g.,dialer application 2020 for a learning session keyed by S11.

Dialer application 2020 can instruct learning model 2030, viacommunication 2056, to set up a prediction interface. As shown in FIG.20B, communication 2058 includes a PredictionInterface( ) call with fourparameters: session key S11, and three parameters for requested outputpredictions: ring tone volume settings, mute settings, and vibrationsettings, as shown in FIG. 20B, using the respective predefinedRING_VOL, MUTE_SET, and VIB_SET values.

In response, learning model 2030 sends communication 2058 to built-infeatures 2040 with a ReqBI( ) function call requesting built-infeatures. As shown in FIG. 20B, learning model 2030 uses the ReqBI( )call to request, using input-source descriptor “bid”, current locationvalues, clock times, calendar-related events, and numbers of local (orproximate) devices, by passing the respective predefined LOC,CLOCK_TIME, CAL_EVENTS, and NUM_LOC_DEVS values to built-in features2040. Built-in features 2040 can include at least the functionality ofbuilt-in features 410 discussed above. In response, built-in features2040 returns a built-in session key BI11 via communication 2060.Built-in session key BI11 is included with subsequent communicationsbetween learning model 2030 and built-in features 2040 to permitaddressing the subsequent built-in related communications to the correctlearning model.

FIG. 20B continues with dialer application 2020 sending communication2062 to learning model 2030 with a Push( ) function call to informlearning model 2030 that a volume settings have been set with a ringtone volume setting of 0, a mute setting of YES or active, and avibration setting of YES. Additionally, built-in features 2040 sendscommunication 2064, with a Push( ) function call to inform learningmodel 2030 that mobile platform 2002 is at location “Home” at a clocktime of 0808 (8:08 AM), no calendar events are active, and that onedevice is nearby (proximate).

FIG. 20B shows a number of communications 2066-2085 for five subsequentvolume setting changes made using dialer application 2020 and built-infeature values provided to learning model 2030 from built-in featureswith the values listed in Table 5 below.

TABLE 5 Commu- Vibra- Cal Local nications Volume Mute tion Location TimeEvents Devices 2066 and 85 NO YES LoudSpot 00:07 NULL 15 2068 2070 and 0YES YES Home 04:08 NULL 0 2072 2074 and 40 NO YES Home 13:22 NULL 0 20762078 and 20 NO YES Work 09:22 NULL 20 2080 2082 and 0 NO YES Work 09:57Meeting 11 2084 10AM

Scenario 2000 continues with learning model 2030 sending communication2086 to dialer application 2020 with a Notify( ) function call. Inscenario 2000, learning model 2030 is trained after receiving the datafrom communications 2082 and 2084. FIG. 20B shows that learning model2030 indicates it is trained by sending communication 2086 to dialerapplication 2020 with a Notify( ) function call including twoparameters: a session key S11, and a predefined value of LM_TRAINED toindicate that learning model 2030 is now trained.

FIG. 20B shows scenario 2000 continuing with dialer application 2020sending communication 2088 a to learning model 2030. Communication 2088a includes the Pull( ) function call to request a prediction of ringervolume, mute, and vibration settings from learning model 2030 usingrespective pre-defined values of RING_VOL, MUTE_SET, and VIB_SET asparameters in the Pull( ) function call. Communication 2088 b includesfeature data from built-in functions 2040 with a location of Work, atime of 2:02 PM (1402), a calendar event of NULL or no calendar eventsactive, and a number of local devices of 7. Learning model 2030 canapply machine-learning operations to predict the requested volumesettings. Scenario 2000 continues with learning model 2030 sending theprediction to dialer application 2020 using PullResp( ) in communication2088 c. FIG. 20B shows that the prediction of communication 2088 cincludes a ringtone volume setting of 20, a mute setting of NO or notactive, and vibration setting of YES.

Scenario 2000 continues with dialer application 2020 sendingcommunication 2090 a to learning model 2030. Communication 2090 aincludes the Pull( ) function call to request a prediction of ringervolume, mute, and vibration settings from learning model 2030 usingrespective pre-defined values of RING_VOL, MUTE_SET, and VIB_SET asparameters the Pull( ) function call. Communication 2090 b includesfeature data from built-in functions 2040 with a location of Work, atime of 2:28 PM (1428), a calendar event of “Meet230PM”, and a number oflocal devices of 5. Learning model 2030 can apply machine-learningoperations to predict the requested volume settings. Scenario 2000continues with learning model 2030 sending the prediction to dialerapplication 2020 using PullResp( ) in communication 2090 c. FIG. 20Bshows that the prediction of communication 2090 c includes a ringtonevolume setting of 0, a mute setting of NO, and vibration setting of YES.

FIG. 20B shows that built-in features 2040 then sends communication 2092a to learning model 2030. Communication 2092 a includes a Push( )function call to inform learning model 2030 that mobile platform 2002 isat “LoudSpot” at 11:58 PM (2358) without any calendar events scheduledand 17 local devices detected. In response to communication 2092 a,learning model 2030 can apply machine-learning operations to predictvolume settings based on the information provided via communication 2092a. Scenario 2000 continues with learning model 2030 sending a Notify( )function call in communication 2092 b to dialer application 2020,predicting a ringtone volume setting of 85, a mute setting of NO, andvibration setting of YES. Upon reception of communication 2092 b, mobileplatform 2002 and/or dialer application 2020 can prompt a user of mobileplatform 2002 of recently-predicted volume settings and/or apply therecently-predicted volume settings, perhaps based on settings of smartvolume setting 2026 and prediction usage setting 2028.

In scenario 2000, built-in features 2040 sends communication 2094 a tolearning model 2030, where communication 2094 a includes a Push( )function call to inform learning model 2030 that mobile platform 2002 isat “Work” at 12:57 PM (1257), with a calendar event of “Meeting1PM”, andno other local mobile platforms present. In response to communication2094 a, learning model 2030 can apply machine-learning operations topredict volume settings based on the information provided viacommunication 2094 a. Scenario 2000 continues with learning model 2030sending a Notify( ) function call in communication 2094 b to dialerapplication 2020, predicting a ringtone volume setting of 0, a mutesetting of YES, and vibration setting of YES. Upon reception ofcommunication 2094 b, mobile platform 2002 and/or dialer application2020 can prompt a user of mobile platform 2002 of recently-predictedvolume settings and/or apply the predicted volume settings, perhapsbased on settings of smart volume setting 2026 and prediction usagesetting 2028.

Scenario 2000 concludes with dialer application 2020 call Save( )function via communication 2096 to request that learning model 2030 savea context, and perhaps other data, of a learning session thread in aVolSetModel variable. In some scenarios not shown in FIG. 20B, learningmodel 2030 can provide a response to communication 2096, such as areturn value to the Save( ) function and/or a communication providing astatus of the Save( ) function; e.g., SaveOK or SaveFail.

Example Data Network

FIG. 21 shows server devices 2108, 2110 configured to communicate, vianetwork 2106, with programmable devices 2104 a, 2104 b, and 2104 c.Network 2106 may correspond to a LAN, a wide area network (WAN), acorporate intranet, the public Internet, or any other type of networkconfigured to provide a communications path between networked computingdevices. The network 2106 may also correspond to a combination of one ormore LANs, WANs, corporate intranets, and/or the public Internet.

Although FIG. 21 only shows three programmable devices, distributedapplication architectures may serve tens, hundreds, or thousands ofprogrammable devices. Moreover, programmable devices 2104 a, 2104 b, and2104 c (or any additional programmable devices) may be any sort ofcomputing device, such as an ordinary laptop computer, desktop computer,network terminal, wireless communication device (e.g., a cell phone orsmart phone), and so on. In some embodiments, programmable devices 2104a, 2104 b, and 2104 c may be dedicated to the design and use of softwareapplications. In other embodiments, programmable devices 2104 a, 2104 b,and 2104 c may be general purpose computers that are configured toperform a number of tasks and need not be dedicated to softwaredevelopment tools.

Server devices 2108, 2110 can be configured to perform one or moreservices, as requested by programmable devices 2104 a, 2104 b, and/or2104 c. For example, server device 2108 and/or 2110 can provide contentto programmable devices 2104 a-2104 c. The content can include, but isnot limited to, web pages, hypertext, scripts, binary data such ascompiled software, images, audio, and/or video. The content can includecompressed and/or uncompressed content. The content can be encryptedand/or unencrypted. Other types of content are possible as well.

As another example, server device 2108 and/or 2110 can provideprogrammable devices 2104 a-2104 c with access to software for database,search, computation, graphical, audio, video, World Wide Web/Internetutilization, and/or other functions. Many other examples of serverdevices are possible as well.

Computing Device Architecture

FIG. 22A is a block diagram of a computing device (e.g., system) inaccordance with an example embodiment. In particular, computing device2200 shown in FIG. 22A can be configured to perform one or morefunctions of mobile platforms 200, 502, 602, 902 server devices 2108,2110, network 2106, and/or one or more of programmable devices 2104 a,2104 b, and 2104 c. Computing device 2200 may include a user interfacemodule 2201, a network communications interface module 2202, one or moreprocessors 2203, and data storage 2204, all of which may be physicallyand/or communicatively linked together via a system bus, network, orother connection mechanism 2205.

Generally, user interface module 2201 is configured to send data toand/or receive data from external user input/output devices. Forexample, user interface module 2201 can be configured to send and/orreceive data to and/or from user input devices such as a keyboard, akeypad, a touch screen, a computer mouse, a track ball, a joystick, acamera, a voice recognition module, and/or other similar devices. Userinterface module 2201 can also be configured to provide output to userdisplay devices, such as one or more cathode ray tubes (CRT), liquidcrystal displays (LCD), light emitting diodes (LEDs), displays usingdigital light processing (DLP) technology, printers, light bulbs, and/orother similar devices, either now known or later developed. Userinterface module 2201 can also be configured generate audible output(s)through device(s) such as a speaker, speaker jack, audio output port,audio output device, earphones, telephone ringers, and/or other similardevices. In some embodiments, user interface module 2210 can beconfigured to provide haptic and/or tactile feedback using one or morevibration devices, tactile sensors, actuators including hapticactuators, tactile touchpads, piezo-haptic devices, piezo-hapticdrivers, and/or other similar devices.

Network communications interface module 2202 can include one or morewireless interfaces 2207 and/or one or more wireline interfaces 2208that are configurable to communicate via a network, such as network 2106shown in FIG. 21. Wireless interfaces 2207 can include one or morewireless transmitters, receivers, and/or transceivers, such as aBluetooth transceiver, a Zigbee transceiver, a Wi-Fi transceiver, aWiMAX transceiver, and/or other similar type of wireless transceiverconfigurable to communicate via a wireless network. Wireline interfaces2208 can include one or more wireline transmitters, receivers, and/ortransceivers, such as an Ethernet transceiver, a Universal Serial Bus(USB) transceiver, or similar transceiver configurable to communicatevia a twisted pair wire, a coaxial cable, a fiber-optic link, or asimilar physical connection to a wireline network.

In some embodiments, network communications interface module 2202 can beconfigured to provide reliable, secured, and/or authenticatedcommunications. For each communication described herein, information forensuring reliable communications (i.e., guaranteed message delivery) canbe provided, perhaps as part of a message header and/or footer (e.g.,packet/message sequencing information, encapsulation header(s) and/orfooter(s), size/time information, and transmission verificationinformation such as CRC and/or parity check values). Communications canbe made secure (e.g., be encoded or encrypted) and/or decrypted/decodedusing one or more cryptographic protocols and/or algorithms, such as,but not limited to, DES, AES, RSA, Diffie-Hellman, and/or DSA. Othercryptographic protocols and/or algorithms can be used as well or inaddition to those listed herein to secure (and then decrypt/decode)communications.

Processors 2203 can include one or more general purpose processorsand/or one or more special purpose processors (e.g., digital signalprocessors, application specific integrated circuits, etc.). Processors2203 can be configured to execute computer-readable program instructions2206 that are contained in the data storage 2204 and/or otherinstructions as described herein.

Data storage 2204 can include one or more computer-readable storagemedia that can be read and/or accessed by at least one of processors2203. The one or more computer-readable storage media can includevolatile and/or non-volatile storage components, such as optical,magnetic, organic or other memory or disc storage, which can beintegrated in whole or in part with at least one of processors 2203. Insome embodiments, data storage 2204 can be implemented using a singlephysical device (e.g., one optical, magnetic, organic or other memory ordisc storage unit), while in other embodiments, data storage 2204 can beimplemented using two or more physical devices.

Data storage 2204 can include computer-readable program instructions2206 and perhaps additional data. In some embodiments, data storage 2204can additionally include storage required to perform at least part ofthe herein-described methods and techniques and/or at least part of thefunctionality of the herein-described devices and networks.

Cloud-Based Servers

FIG. 22B depicts a network 2106 of computing clusters 2209 a, 2209 b,2209 c arranged as a cloud-based server system in accordance with anexample embodiment. Server devices 2108 and/or 2110 of FIG. 21 can becloud-based devices that store program logic and/or data of cloud-basedapplications and/or services. In some embodiments, server devices 2108and/or 2110 can be a single computing device residing in a singlecomputing center. In other embodiments, server device 2108 and/or 2110can include multiple computing devices in a single computing center, oreven multiple computing devices located in multiple computing centerslocated in diverse geographic locations. For example, FIG. 21 depictseach of server devices 2108 and 2110 residing in different physicallocations.

In some embodiments, data and services at server devices 2108 and/or2110 can be encoded as computer readable information stored innon-transitory, tangible computer readable media (or computer readablestorage media) and accessible by programmable devices 2104 a, 2104 b,and 2104 c, and/or other computing devices. In some embodiments, data atserver device 2108 and/or 2110 can be stored on a single disk drive orother tangible storage media, or can be implemented on multiple diskdrives or other tangible storage media located at one or more diversegeographic locations.

FIG. 22B depicts a cloud-based server system in accordance with anexample embodiment. In FIG. 22B, the functions of server device 2108and/or 2110 can be distributed among three computing clusters 2209 a,2209 b, and 2209 c. Computing cluster 2209 a can include one or morecomputing devices 2200 a, cluster storage arrays 2210 a, and clusterrouters 2211 a connected by a local cluster network 2212 a. Similarly,computing cluster 2209 b can include one or more computing devices 2200b, cluster storage arrays 2210 b, and cluster routers 2211 b connectedby a local cluster network 2212 b. Likewise, computing cluster 2209 ccan include one or more computing devices 2200 c, cluster storage arrays2210 c, and cluster routers 2211 c connected by a local cluster network2212 c.

In some embodiments, each of the computing clusters 2209 a, 2209 b, and2209 c can have an equal number of computing devices, an equal number ofcluster storage arrays, and an equal number of cluster routers. In otherembodiments, however, each computing cluster can have different numbersof computing devices, different numbers of cluster storage arrays, anddifferent numbers of cluster routers. The number of computing devices,cluster storage arrays, and cluster routers in each computing clustercan depend on the computing task or tasks assigned to each computingcluster.

In computing cluster 2209 a, for example, computing devices 2200 a canbe configured to perform various computing tasks of server device 2108.In one embodiment, the various functionalities of server device 2108 canbe distributed among one or more of computing devices 2200 a, 2200 b,and 2200 c. Computing devices 2200 b and 2200 c in computing clusters2209 b and 2209 c can be configured similarly to computing devices 2200a in computing cluster 2209 a. On the other hand, in some embodiments,computing devices 2200 a, 2200 b, and 2200 c can be configured toperform different functions.

In some embodiments, computing tasks and stored data associated withserver devices 2108 and/or 2110 can be distributed across computingdevices 2200 a, 2200 b, and 2200 c based at least in part on theprocessing requirements of server devices 2108 and/or 2110, theprocessing capabilities of computing devices 2200 a, 2200 b, and 2200 c,the latency of the network links between the computing devices in eachcomputing cluster and between the computing clusters themselves, and/orother factors that can contribute to the cost, speed, fault-tolerance,resiliency, efficiency, and/or other design goals of the overall systemarchitecture.

The cluster storage arrays 2210 a, 2210 b, and 2210 c of the computingclusters 2209 a, 2209 b, and 2209 c can be data storage arrays thatinclude disk array controllers configured to manage read and writeaccess to groups of hard disk drives. The disk array controllers, aloneor in conjunction with their respective computing devices, can also beconfigured to manage backup or redundant copies of the data stored inthe cluster storage arrays to protect against disk drive or othercluster storage array failures and/or network failures that prevent oneor more computing devices from accessing one or more cluster storagearrays.

Similar to the manner in which the functions of server devices 2108and/or 2110 can be distributed across computing devices 2200 a, 2200 b,and 2200 c of computing clusters 2209 a, 2209 b, and 2209 c, variousactive portions and/or backup portions of these components can bedistributed across cluster storage arrays 2210 a, 2210 b, and 2210 c.For example, some cluster storage arrays can be configured to store thedata of server device 2108, while other cluster storage arrays can storedata of server device 2110. Additionally, some cluster storage arrayscan be configured to store backup versions of data stored in othercluster storage arrays.

The cluster routers 2211 a, 2211 b, and 2211 c in computing clusters2209 a, 2209 b, and 2209 c can include networking equipment configuredto provide internal and external communications for the computingclusters. For example, the cluster routers 2211 a in computing cluster2209 a can include one or more internet switching and routing devicesconfigured to provide (i) local area network communications between thecomputing devices 2200 a and the cluster storage arrays 2201 a via thelocal cluster network 2212 a, and (ii) wide area network communicationsbetween the computing cluster 2209 a and the computing clusters 2209 band 2209 c via the wide area network connection 2213 to network 2106.Cluster routers 2211 b and 2211 c can include network equipment similarto the cluster routers 2211 a, and cluster routers 2211 b and 2211 c canperform similar networking functions for computing clusters 2209 b and2209 b that cluster routers 2211 a perform for computing cluster 2209 a.

In some embodiments, the configuration of the cluster routers 2211 a,2211 b, and 2211 c can be based at least in part on the datacommunication requirements of the computing devices and cluster storagearrays, the data communications capabilities of the network equipment inthe cluster routers 2211 a, 2211 b, and 2211 c, the latency andthroughput of local networks 2212 a, 2212 b, 2212 c, the latency,throughput, and cost of wide area network links 2213 a, 2213 b, and 2213c, and/or other factors that can contribute to the cost, speed,fault-tolerance, resiliency, efficiency and/or other design goals of themoderation system architecture.

CONCLUSION

The above detailed description describes various features and functionsof the disclosed systems, devices, and methods with reference to theaccompanying figures. In the figures, similar symbols typically identifysimilar components, unless context dictates otherwise. The illustrativeembodiments described in the detailed description, figures, and claimsare not meant to be limiting. Other embodiments can be utilized, andother changes can be made, without departing from the spirit or scope ofthe subject matter presented herein. It will be readily understood thatthe aspects of the present disclosure, as generally described herein,and illustrated in the figures, can be arranged, substituted, combined,separated, and designed in a wide variety of different configurations,all of which are explicitly contemplated herein.

With respect to any or all of the ladder diagrams, scenarios, and flowcharts in the figures and as discussed herein, each block and/orcommunication may represent a processing of information and/or atransmission of information in accordance with example embodiments.Alternative embodiments are included within the scope of these exampleembodiments. In these alternative embodiments, for example, functionsdescribed as blocks, transmissions, communications, requests, responses,and/or messages may be executed out of order from that shown ordiscussed, including substantially concurrent or in reverse order,depending on the functionality involved. Further, more or fewer blocksand/or functions may be used with any of the ladder diagrams, scenarios,and flow charts discussed herein, and these ladder diagrams, scenarios,and flow charts may be combined with one another, in part or in whole.

A block that represents a processing of information may correspond tocircuitry that can be configured to perform the specific logicalfunctions of a herein-described method or technique. Alternatively oradditionally, a block that represents a processing of information maycorrespond to a module, a segment, or a portion of program code(including related data). The program code may include one or moreinstructions executable by a processor for implementing specific logicalfunctions or actions in the method or technique. The program code and/orrelated data may be stored on any type of computer readable medium suchas a storage device including a disk or hard drive or other storagemedium.

The computer readable medium may also include non-transitory computerreadable media such as computer-readable media that stores data forshort periods of time like register memory, processor cache, and randomaccess memory (RAM). The computer readable media may also includenon-transitory computer readable media that stores program code and/ordata for longer periods of time, such as secondary or persistent longterm storage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable media may also be any other volatile or non-volatile storagesystems. A computer readable medium may be considered a computerreadable storage medium, for example, or a tangible storage device.

Moreover, a block that represents one or more information transmissionsmay correspond to information transmissions between software and/orhardware modules in the same physical device. However, other informationtransmissions may be between software modules and/or hardware modules indifferent physical devices.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. A method, comprising: receiving feature-relateddata at a machine-learning service executing on a mobile platform,wherein the feature-related data comprises usage-related data about oneor more durations that the mobile platform is activated andplatform-related data received from the mobile platform, and wherein theusage-related data and the platform-related data differ; determiningwhether the machine-learning service is trained to performmachine-learning operations related to predicting a duration that themobile platform will be activated; and in response to determining thatthe machine-learning service is trained: receiving, at themachine-learning service, a request for a predicted duration that themobile platform will be activated, determining the predicted duration bythe machine-learning service performing a machine-learning operation onthe feature-related data, selecting one or more media files from aplurality of media files based on the predicted duration by at least:determining a total duration based on an associated duration of eachmedia file of the one or more media files, and selecting the one or moremedia files based on the total duration by at least: determining whetherthe total duration is less than the predicted duration; and afterdetermining that the total duration is less than the predicted duration,selecting the one or more media files; and presenting the selected oneor more media files using the mobile platform.
 2. The method of claim 1,wherein the machine-learning operations comprise a classificationoperation.
 3. The method of claim 2, wherein the predicted durationcomprises a predicted classification of the duration.
 4. The method ofclaim 3, wherein the predicted classification of the duration isselected from among a short duration, a medium duration, and a longduration.
 5. The method of claim 1, wherein the machine-learningoperations comprise a regression operation.
 6. The method of claim 5,wherein the regression operation comprises a linear regressionoperation.
 7. The method of claim 5, wherein the predicted durationcomprises a predicted time span associated with the duration.
 8. Anarticle of manufacture including a non-transitory computer-readablestorage medium having instructions stored thereon that, when executed bya processor, cause the processor to perform functions comprising:receiving feature-related data at a machine-learning service executingon the processor, wherein the feature-related data comprisesusage-related data about one or more durations that the article ofmanufacture is activated and platform-related data, and wherein theusage-related data and the platform-related data differ; determiningwhether the machine-learning service is trained to performmachine-learning operations related to predicting a duration that thearticle of manufacture will be activated in response to determining thatthe machine-learning service is trained: receiving, at themachine-learning service, a request for a predicted duration that thearticle of manufacture will be activated, determining the predictedduration by the machine-learning service performing a machine-learningoperation on the feature-related data, selecting one or more media filesbased on the predicted duration by at least: determining a totalduration based on an associated duration of each media file of the oneor more media files, and selecting the one or more media files based onthe total duration by at least: determining whether the total durationis less than the predicted duration; and after determining that thetotal duration is less than the predicted duration, selecting the one ormore media files; and presenting the selected one or more media files.9. The article of manufacture of claim 8, wherein the machine-learningoperation comprises a classification operation, and wherein thepredicted duration comprises a predicted classification of a duration.10. The article of manufacture of claim 9, wherein the predictedclassification of the duration is selected from among a short duration,a medium duration, and a long duration.
 11. The article of manufactureof claim 8, wherein the machine-learning operation comprises aregression operation, and wherein the predicted duration comprises apredicted time span associated with the predicted duration.
 12. Thearticle of manufacture of claim 11, wherein the regression operationcomprises a linear regression operation.
 13. A mobile platform,comprising: a processor; and a computer-readable storage medium,configured to store instructions that, when executed by the processor,cause the mobile platform to perform functions comprising: receivingfeature-related data at a machine-learning service executing on theprocessor, wherein the feature-related data comprises usage-related dataabout one or more durations that the mobile platform is activated andplatform-related data, and wherein the usage-related data and theplatform-related data differ; determining whether the machine-learningservice is trained to perform machine-learning operations related topredicting a duration that the mobile platform will be activated; inresponse to determining that the machine-learning service is trained:receiving, at the machine-learning service, a request for a predictedduration that the mobile platform will be activated, generating thepredicted duration by the machine-learning service performing amachine-learning operation on the feature-related data, selecting one ormore media files from a plurality of media files based on the predictedduration by at least: determining a total duration based on anassociated duration of each media file of the one or more media files,and selecting the one or more media files based on the total duration byat least: determining whether the total duration is less than thepredicted duration; and after determining that the total duration isless than the predicted duration, selecting the one or more media files;and presenting the selected one or more media files using the mobileplatform.
 14. The mobile platform of claim 13, wherein themachine-learning operation comprises a classification operation, andwherein the predicted duration comprises a predicted classification of aduration.
 15. The mobile platform of claim 14, wherein the predictedclassification of the duration is selected from among a short duration,a medium duration, and a long duration.
 16. The mobile platform of claim13, wherein the machine-learning operation comprises a regressionoperation, and wherein the predicted duration comprises a predictedamount for the predicted duration.
 17. The mobile platform of claim 16,wherein the regression operation comprises a linear regressionoperation.